Journal of Agricultural Machinery

Abstract Introduction
Water is a very important component of many food products and determines their physical properties, texture, sensory quality, and rate of chemical and microbiological reactions. Magnetic fields, as an emerging technological tool, have recently received increasing attention in the food industry due to their strong permeability and non-contact nature. Studies have shown that magnetic fields weaken hydrogen bonds. Researchers reported that when the magnetic field strength increases, the refractive index of water increases by approximately 0.1%. Magnetic fields can also weaken the van der Waals bonds between water molecules. A similar type of magnet was used in another study for a magnetic field of 6 Tesla. They did not evaluate the evaporation rate, but rather some other properties using the air flow contact angle, and suggested that the magnetization of pure water requires air and the relative motion of the water against the magnetic flux. Previous experiments were conducted at room temperature. The effects of magnetic fields on water samples have been studied from various aspects and are still of interest to researchers in this field. The direction of air flow relative to the magnetic field gradient also affects the evaporation rate. However, some experiments are not well-defined, and their repetition will not be easily feasible. Therefore, a review of the literature on the effects of magnetic fields on water properties shows that there is still no coherent view on the mechanism of the effects of such fields. In this study, we focused on studying the effect of a static electromagnetic field with predefined intensities on the water evaporation rate, fields from 30 to 130 mT and a temperature range between 30, 50, and 70 °C with forced air movement at a uniform speed, and the continuous presence of samples in the electromagnetic field, which, to our knowledge, has not been reported before. To this end, the objectives of this study include: (1) quantitative determination of the evaporation rate as a function of the applied magnetic field; (2) finding the energy contribution to the evaporation rate in the presence of a magnetic field.
Materials and Methods
To create a magnetic field, two copper coils with a wire gauge of 1.25 mm, a core diameter of 110 mm, and 2500 turns were used. To measure the level of magnetism, the PHYWE Tesla meter with an accuracy of 10 microteslas and measurement range of 20 to 2000 mT, made in Germany, was used. To measure the weight of the samples at the desired intervals, the AND digital scale model GF6000 with a weighing capacity of 6000 grams and an accuracy of 0.01 grams, made in Japan, was used. For each of the tests, 40 milliliters of Type II distilled water were used in accordance with ASTM D1193 and ISO 3696 standards, with a conductivity of 0.1 μS.cm^-1. Initially, to ensure uniform testing conditions, the device was operated for 15 minutes, after which the samples were placed in petri dishes with a diameter of 90 millimeters and a height of 11 millimeters at a constant temperature of 20 degrees Celsius and prepared for testing. After preparing the samples and the device, the prepared samples were placed inside the device and removed at 15-minute intervals for a duration of 120 minutes, then weighed using a scale with an accuracy of 0.01 grams. This process was carried out separately for each treatment, and the data were collected. The evaporation rate of the sample per unit time was calculated using the unit of milligrams per minute and the trend line equation. The slope of the obtained lines indicated the evaporation rate values. All the trend lines obtained had a coefficient of determination (i.e., linear correlation degree) equal to or greater than 0.99. We chose the magnetic field range of 30 to 130 mT because the working range of the magnetic field generator in the device fell within this range. The experiments were conducted using a factorial test based on a completely randomized design with two replications. The first factor was the intensity of the electromagnetic field at four levels: 0, 30, 60, and 130 mT; the second factor was temperature at three levels: 30, 50, and 70 degrees Celsius; and the third factor was time at eight levels: 15 to 120 minutes. The means were compared at the 5% significance level using Duncan's test. For this purpose, SAS software version 9.2 was used, and Excel 2016 was used for plotting the graphs.
Results and Discussions
The samples were placed in the field generated by the Helmholtz coil, and the results confirmed the effect of the magnetic field on the water evaporation rate. It was demonstrated in a study that, although increasing temperature and decreasing humidity are the dominant factors affecting the rate of water evaporation, a stationary magnetic field with decreasing temperature has an increasing effect on the evaporation rate. This finding contradicts the results of the present study, where the experimental data indicate an increased impact of the magnetic field with rising temperature levels. Considering the results of the analysis of variance, all factors along with their two-way and three-way interactions were significant at the one percent level.
Based on Duncan's multiple range test, for duration, magnetic intensity, and temperature, with the increase in each factor level, the weighted evaporation values of the samples significantly decreased compared to the previous factor level. All the trend lines obtained had a coefficient of determination (i.e., linear correlation degree) equal to or greater than 0.99. The slope of the line equation between weight and time is equal to the evaporation rate (R). From the evaporation rates obtained from experimental data, it is clear that the correlation with temperature is not linear, but rather an exponential function as:
The above model can behave like a linear model. The parameter estimates of the model were obtained using the SPSS software as:
The final model can be expressed in the following form:
At a temperature of 30 degrees Celsius, the energy consumption decreased by 11.4 kJ with the increase of magnetic levels. At temperatures of 50 and 70 degrees Celsius, the reduction in energy consumption with the application of a magnetic field was observed to be 48.3 and 45.2 kJ per gram, respectively. These results demonstrate the effect of magnetism on optimizing energy consumption at different temperature levels, with 50 degrees Celsius and a magnetic field intensity of 130 mT being the optimal conditions in terms of energy consumption.
Conclusion
In this study, a statistical approach was used to investigate the rate of water evaporation under different magnetic fields and temperatures over a specified period. The results indicated that the magnetic field, like temperature, affects water evaporation, and as the field increased, the rate of water evaporation also rose. Specifically, the evaporation rates in the treatments at 30, 50, and 70 degrees Celsius after 120 minutes without applying the magnetic field were 43.7%, 53.3%, and 66.5% of the initial weight of the sample, respectively. After applying the magnetic field from 0 to 130 mT, the evaporation rates were reported as 59.6%, 82.8%, and 94.7% of the initial sample weight, respectively, indicating an increase in the evaporation rate with the application of the magnetic field. Finally, a model was proposed that accurately predicts this trend and can be utilized. The analysis of the energy consumption results for each treatment also showed that the magnetic field can influence the total energy consumption for water evaporation and optimize energy use, with reductions of 14.6% at 30 degrees Celsius, 26.55% at 50 degrees Celsius, and 22.5% at 70 degrees Celsius.
Acknowledgments
The present study pertains to research project number 60993 approved by Ferdowsi University of Mashhad, and it acknowledges the efforts of Dr. Mohammad Farkhari (Associate Professor of Plant Breeding at the University of Agricultural Sciences and Natural Resources of Khuzestan) and Dr. Omid Doosti Irani, alumnus of the Biosystems Engineering Department at Ferdowsi University of Mashhad.

Abstract Agricultural mechanization is a vital driver of productivity, food security, and sustainable farming in Zimbabwe and worldwide. Its transformative potential is increasingly recognized as essential for meeting food demand and fostering resilient rural economies. Yet, smallholder farmers face persistent barriers that limit access to modern technologies. Since the land reform era, systemic inefficiencies, inequalities, and weak institutional support have exacerbated these challenges. This study employed a narrative literature review, complemented by scoping techniques, to synthesize data from peer-reviewed publications, policy reports, and institutional documents (2000–2024). Thematic analysis revealed that only 12% of Zimbabwean smallholders use tractors, compared to 80% of large-scale farmers. Mechanization can double yields, as seen in Zimbabwe (100% increase) and up to 150% in comparable countries. However, regional disparities remain stark, ranging from 5–15% in arid provinces to over 50% in more productive areas. Opportunities lie in localized manufacturing hubs, climate-resilient technologies, and new financing models. Drawing on lessons from other African nations, this study highlights strategies such as improving policy coherence, expanding financing, and fostering public-private partnerships.

Abstract This research seeks to determine the highest possible yield by integrating wastewater treatment plant sludge with food waste from plate scraps at Adama Science and Technology University (ASTU) in Ethiopia. Feedstock characterization and biogas co-generation were done on different Plate Scrap (PS), Wastewater Treatment Plant Sludge (WTPS), and 100 ml cow manure combination ratios. The feedstocks were evaluated for their TS and MC before combination, and TS, VS, TDS, COD, BOD, and pH after combination. This experiment was done in two rounds using three water baths and twenty-seven Batch Reactors (BR) with 2.5 L volume each. In the first round, eighteen reactors were used, and nine were used in the second experiment. Triplicate testing was used to evaluate the feedstock sample characteristics and to run the experiment. The reactors were operated for thirty-five days at a hydraulic retention time and a temperature of 50 °C. The daily biogas yield using the water displacement method, total biogas yield, and methane composition were measured and reported. Three sub-reactors were considered to find the average biogas yield of individual reactors. A notable increase in both daily and total biogas yield was observed with the reactor composition of 75% PS Injera (PSI) flat bread and 25% WTPS. The daily maximum and the average biogas yields were 220 mL and 810 mL, with the TS of 55,066 mg L^-1 and the VS of 51,000 mg L^-1. The maximum methane inside the produced biogas was 68%, from PSI75% and WTPS25%. This combination also showed the highest biogas yield.

Abstract The mechanization index and farm power density are the most significant parameters that highlight the extent of mechanization, and they are estimated using data collected through questionnaires from smallholder farmers and machinery service providers in the Bure district, Amhara Region, Ethiopia. The insights obtained from the data reveal the current availability of various machinery for a range of farming activities, along with the methods farmers adopt to fit their land’s size, topological features, elevation, crop types, and the reasons for the inadequate use of machinery. The cost data for different farm operations, categorized by animal, human, and mechanical power, are used to estimate the mechanization index and farm power density. The mechanization index indicates that threshing and cleaning have a rate of 19.01%, whereas land preparation and clearing stand at 1.94%. Crop-wise mechanization index for maize is 6.66%, 1.90% for wheat, and 0.69% for pepper, with an average index of 1.32%. The power density is estimated as 0.12 kW ha^-1, which is expected to reach 1 kW ha^-1, the goal set for 2024. Tillage is found to be the most power-intensive activity, with 32.12% of the total energy expenditure in crop production. The calculated tractor density is 17 tractors per 10,000 hectares of arable land, which is comparable to the continental average in Africa of 20 tractors per 10,000 hectares. The lower values of the mechanization index and farm power density identified from the survey indicate the need for support farmers in terms of subsidies and increased availability of machinery. Consolidation of land can boost farm mechanization, reduce the cost of production, and increase productivity. The present research contributes to the estimation of mechanization index, power density, and tractor density in comparison to the target set by the Ethiopian government, and the approach can be scaled up to other parts of the country as well.

Abstract This research focused on creating an IoT-enabled color-sorting machine for Robusta coffee cherries, utilizing image processing as an effective alternative to manual sorting. The system tackles a significant issue with the strip-picking harvesting method, which gathers cherries at different ripeness levels, negatively affecting coffee quality. The machine sorts cherries by ripeness—red for ripe, green for unripe, and black for overripe—using a detection model trained through image processing and implemented on a Raspberry Pi 4 Model B. The performance was assessed based on sorting speed and classification accuracy. The detection model successfully identified 277 out of 300 cherries, resulting in an overall classification accuracy of 92.33% and a mean precision of 92.55%. In practical tests with 100 cherries over 10 trials, the machine achieved an average sorting accuracy of 86.83% and a mean sorting time of 21 minutes and 33 seconds. When compared to a previously developed coffee bean sorter, the new device showed improved accuracy and faster processing speed.

Abstract Residual plastic mulch film pollution in agricultural fields threatens soil health and sustainable agriculture due to structural degradation and inefficient recovery. To address this, this study investigated the effects of mulch film thickness (0.006-0.014 mm), mulching duration (0-120 days), and two contrasting ecological regions in the Guizhou Province of China: Longgang Town (Kaiyang County) and Linquan Town (Qianxi County), on physical properties and recyclability in tobacco cultivation. Analyses of mechanical, optical, and recycling efficiency revealed that tensile, tear, and puncture strengths increase proportionally with thickness across identical durations, while elongation rates initially increase and then decline. Prolonged mulching reduces mechanical performance at fixed thicknesses, with longitudinal tensile and tear strengths consistently exceeding transverse values. Optical properties vary significantly: unused films exhibit peak light transmittance and haze, while 0.008 mm films achieve maximum transmittance, and thicker films (0.010-0.014 mm) show higher haze. Recycling efficiency correlates positively with thickness and inversely with mulching duration. After 120 days, recycling efficiency strongly correlates with longitudinal and transverse tear loads. Regional variations significantly affect the mechanical properties of 0.010 mm films, suggesting that 0.010 mm films may adapt better to diverse environments. Thicker films show higher recyclability after 120 days of mulching due to retained structural integrity. These findings systematically link physical degradation patterns to recyclability under field conditions, offering actionable insights for optimizing mulch film use, designing durable products, and improving recovery machinery. The study supports sustainable agricultural practices by balancing film performance, environmental adaptability, and end-of-life recovery efficiency.

Abstract The study of soil behaviour in wheel interaction is complex due to the wheel's geometry and the varying soil conditions. Traditional measurements of soil parameters, such as the Bevameter and the cone penetrometer, are time-consuming and labour-intensive. This research presents a machine learning-based approach to predict soil sinkage in plate penetration tests, providing a suitable alternative to conventional methods. A soil bin with controlled experimental conditions was used to collect data, which was measured by a load cell and a magnetic encoder at a constant penetration rate of 4 mm s^-1. Two main machine learning models were selected; XGBoost and CatBoost. Hybrid versions of these models were developed using the Shrike Bird Optimisation Algorithm (SBOA). The results showed that the hybrid models outperformed the base models. The SBOA-CatBoost hybrid model achieved the highest accuracy on the training data with a coefficient of determination of 0.99, a mean square error of 2.81, and a mean absolute error of 0.79. The findings of this study highlight the potential of machine learning as a cost-effective and efficient alternative to traditional methods for measuring soil parameters. Further research is recommended to validate these models in different soil types and conditions.

Abstract Introduction
Paying attention to the technical aspects of production plays a crucial role in increasing yield and ensuring sustainable agriculture. Organic fertilizers, such as poultry manure, contribute to plant growth by providing essential nutrients and improving soil quality. However, they alone cannot fully meet the nutritional needs of plants. The combination of organic and chemical fertilizers is an effective approach to enhancing soil fertility and boosting crop performance, ultimately leading to sustainable agricultural development. Integrated nutrient management also helps reduce the use of chemical fertilizers while minimizing their harmful effects on the environment. Potassium is an essential element in plant nutrition, playing a key role in processes such as photosynthesis, growth, chlorophyll production, and transpiration regulation. Additionally, under stress conditions, potassium enhances water uptake and regulates osmotic pressure, helping to maintain plant health. Potassium fertilizers are classified into two categories: chloride-based and chloride-free. Potassium sulfate, due to its lack of chloride, is a suitable option for chloride-sensitive crops such as tea, potatoes, and sugar beets. Meanwhile, hyperspectral imaging has emerged as an innovative technique with broad applications in detecting chemical parameters, assessing quality, and analyzing the purity of agricultural and food products. This study utilizes hyperspectral image processing technology to determine the pH level of potassium sulfate.
Materials and Methods
The present study was conducted in the Image Processing Laboratory at the Ilam University, Iran. To determine the pH level of potassium sulfate, four different levels of 2.5, 2.6, 2.8, and 2.9 were considered. The pH measurement was performed in the laboratory using a flame photometer. The required images were obtained through hyperspectral imaging using the line-scan method. For each pH level, three samples were obtained and six hyperspectral images were captured for each sample, resulting in 18 images per pH level and a total of 72 hyperspectral images for each pH level. MATLAB software was used for the analysis and processing of these images. The image processing stage included wavelength selection, feature extraction, and feature selection. Finally, the selected features were classified using an artificial neural network.
Results and Discussion
Principal Component Analysis performed on the hyperspectral image channels of potassium sulfate revealed significant variations in the principal component values across different pH levels. This finding indicates that pH conditions exert a considerable influence on the spectral response of the samples. Based on the prominent peaks obtained from the analysis, the most relevant channels were identified, and their corresponding wavelengths were determined as the optimal spectral bands. The selected channels for the four pH levels were 65, 327, 334, 482, 510, 607, and 644, with their corresponding effective wavelengths being 453.32, 669.95, 675.74, 798.11, 821.26, 901.47, and 932.06 nm, respectively. To extract discriminative spectral information, six features were computed from each of the selected wavelengths. Consequently, a total of 42 features were obtained, which were subsequently employed in the classification process of different pH levels. The confusion matrices of the classification model based on the artificial neural network were obtained to evaluate the model's accuracy. The classification accuracy for detecting the pH level of potassium sulfate was 98.6% with effective features and 97.2% without them.
Conclusion
The results of this study demonstrated the high potential of hyperspectral imaging technology combined with the artificial neural network classification method, using strategies with and without effective feature selection, in detecting the pH level of potassium sulfate. The proposed method offers several advantages over laboratory-based approaches, such as being non-destructive, having high speed, and being cost-effective. It is suggested to explore other methods for classifying hyperspectral images for determining the pH level of potassium sulfate. The proposed method in this study could also be applied in the future to identify various chemical elements in potassium sulfate.

Abstract Introduction
Drying is one of the important steps in starch modification, after applying the modification treatments. Starch is obtained from the seeds and fruits of various plants and used in a dried state to achieve a longer shelf life, potentially saving on transportation and storage costs for commercial purposes. Drying is the final necessary step in starch modification, often performed using a conventional oven, a freeze dryer, or an organic solvent (typically ethanol or acetone). In food drying processes, energy consumption is considered a key parameter. The method used to dry pre-gelatinised starch is crucial, as drying is one of the most important steps in the production of modified starch powder. On the other hand, considering the global tendency to use renewable energies, especially in food drying, to reduce thermal damage, energy consumption and drying time, it is of great importance to investigate drying with reflectance window systems, which are environmentally friendly, have high efficiency and cause less damage to the food product components. The effect of drying modified starch with cold plasma by a reflectance window system at a temperature of 50 °C was investigated, and its results were compared with data from the traditional oven drying system.
 Materials and Methods
Potato starch powder was obtained from Zamen Food Products Manufacturing Company, located in Mashhad Industrial City, Iran, in plastic packs. A laboratory-scale cold plasma generator device available at the Sari University of Agricultural Sciences and Natural Resources Growth Centre was used. This device consists of two main parts: the cold plasma generation section and the sample storage section. The device generated cold plasma through direct contact of the sample with the resulting ionised air. Cold plasma was applied to the sample produced by a plasma reactor with copper and steel electrodes at a voltage of 20 kV, a current of 3 mA, and a frequency of 50 Hz, using atmospheric air. A randomised complete factorial design was implemented with the factors of pre-gelatinisation temperature (55 and 60 ℃), cold plasma treatment time (0, 15, and 30 min), and starch drying temperature in the oven (60, 70, and 80 ℃). To prepare pre-gelatinised samples, 10 g of starch was dissolved in 90 g of distilled water to prepare a 10% (w/w) solution. The energy analysis included calculations of drying efficiency, energy efficiency, thermal efficiency, and specific heat consumption. The resulting data were optimized using Design-Expert software.
Results and Discussion
The results showed that the pre-gelatinisation temperature had a significant effect on all the studied parameters (energy, drying, and temperature efficiency), with a confidence level of p < 0.05. Drying temperature did not significantly affect energy efficiency, but it had a significant impact on both drying efficiency and temperature efficiency. Plasma treatment had a substantial effect on energy efficiency and drying efficiency, but no significant effect was observed on temperature efficiency. Based on regression models, the linear model has the best fit to the experimental data and was able to accurately predict the responses, which indicates the importance of the factors under study in process optimisation. Based on optimisation analysis, the optimal conditions indicate a temperature of 60 ℃ for pre-gelatinisation, 70 ℃ for oven-drying, and 30 min for cold plasma treatment time. This combination results in maximum efficiency and reduced energy consumption.
Conclusion
This analysis shows that the studied temperature changes and different treatments have distinct effects on drying processes and energy consumption, which can be considered in optimising these processes. The results of this research can help improve starch production processes and increase their efficiency in related industries. This research simultaneously investigates two new methods for modifying and drying starch, which can result in practical improvements to starch quality.
Funding Sources: This research was funded by the Sari Agricultural Sciences and Natural Resources University in the form of a Master's thesis with registration number 6490/1403/D and registration date 16/9/2024 from the research budget related to the thesis grant.
Conflict of Interest: No conflict of interest has been declared by the authors.
Acknowledgements: We would like to thank Sari Agricultural Sciences and Natural Resources University for their financial and moral support in conducting this research.

Abstract Grain loss and impact damage are key indicators of wheat threshing quality. To explore the mechanisms of grain loss and damage, this study reproduces the wheat threshing process by establishing a discrete element model of wheat plants and a simulation platform for threshing devices. It conducts simulations on the movement laws of material flow and distribution laws of threshed materials under different conditions of feed rate, drum rotational speed, and deflector angle. Based on simulation calculations, the average velocity and force laws of wheat plants were obtained, and the influence laws of feed rate, drum rotational speed, and deflector angle on the threshing process were analysed. Through multi-objective parameter optimisation analysis, it is determined that when the feed rate is 7 kg s^-1, the drum rotational speed is 815 r min^-1, and the deflector angle is 70 degrees, the threshing performance of the device is relatively superior. Bench verification tests before and after optimisation showed that the impurity rate of wheat decreased from 29.19% to 25.02%, and the loss rate decreased from 1.61% to 0.95%, with the error between the model prediction results and the experimental results being less than 5%. The proposed model and optimisation strategy can directly guide the structural improvement of axial-flow threshing devices, significantly shorten the research and development cycle of harvesting equipment, and provide a reliable technical basis for efficient and low-loss wheat harvesting.

Abstract Soybeans serve as a crucial grain, oil, and cash crop in China, yet the nation currently suffers from alarmingly low self-sufficiency rates. The sowing process, being the most critical phase of soybean production, directly determines crop yield and quality. Notably, the suboptimal performance of precision seed meters remains the primary bottleneck limiting yield enhancement due to issues with seeding accuracy. This study aims to analyse the current state of soybean production, highlight technological advancements in seed metering devices, and propose improvement strategies to enhance sowing quality and reduce import dependence, thereby providing a theoretical foundation. This paper systematically retrieves literature from databases such as CNKI, Web of Science, Elsevier, and IEEE Xplore, integrating journal articles, patent documents, and industry reports published between 2000 and 2024. It conducts a comparative analysis of the types, working principles, and research progress of soybean seed metering devices in China and abroad. Results showed that domestic seed metering devices are primarily mechanical, suffering from issues such as excessive seed damage and missed seeding rates, whereas foreign pneumatic seed metering devices offer high precision but are costly and lack adaptability.

Abstract Mechanisation is crucial for enhancing agricultural productivity and operational efficiency, particularly in semi-arid regions. This study evaluated the performance of a CLAAS Talos 220 two-wheel-drive tractor equipped with a Shaktiman rotavator plough at tillage depths of 10 and 15 cm and forward speeds of 3, 5, and 7 km·h^-1 in northern Iraq during the 2023–2024 cropping season. The results showed that increasing depth and speed led to higher power losses resulting from wheel slippage, increased fuel consumption, and decreased field efficiency and actual ploughing depth. The highest power loss (6.96 hp) and lowest efficiency (66.60%) were recorded at a depth of 15 cm and a speed of 7 km·h^-1, while the lowest power loss (0.031 hp) and the highest efficiency (80.42%) were recorded at a depth of 10 cm and a speed of 3 km·h^-1. It was also shown that fuel consumption increases with depth, but decreases at higher speeds, and that the actual depth of ploughing decreases due to vibrations. The results indicate that operating at an average depth of approximately 10 cm and at an average speed of about 5 km·h^-1 is the optimal choice for energy use, improving field performance, and enhancing soil conservation.

Abstract In recent years, the adoption of agricultural tractors has advanced farm mechanisation, with the three-point hitch (TPH) system playing an important role in attaching implements. This study focuses on optimising the geometry of the TPH for the Massey Ferguson 475 (MF475) tractor through simulation in SolidWorks software and validation with laboratory measurements. The independent parameters, including (1) lift arm, (2) lift rod, (3) lower arm lengths, and (4) the distance between the lift rod-lower arm connection point and the lower arm pivot point, were systematically varied to find the optimal design. Additionally, we analysed the effects of the independent parameters on performance parameters such as virtual hitch point positions, mechanical advantage, and lifting force. Results indicated that the existing TPH of the MF475 tractor exhibits discrepancies from the ASABE standard, while the optimised design complies with it. The results showed that the length of the lower arms has the greatest influence on the position of the virtual hitch point. Additionally, the increase in the lengths of the lift arm, lift rod, and lower arm led to a decrease in the lifting forces. In contrast, the increase in the distance between the lift rod-lower arm connection point and the lower arm pivot point led to an increase in the lifting forces. Sensitivity analysis revealed that the distance between the lift rod-lower arm connection point and the lower arm pivot point is the most influential factor affecting lifting force and mechanical advantage.

Abstract Introduction
Traditional methods for evaluating fruit quality, such as pH measurement, are often destructive, time-consuming, and costly, leading to product loss and reduced efficiency in the supply chain. The growing need for rapid, accurate, and non-destructive methods makes the use of technologies like Hyperspectral Imaging (HSI) essential. HSI combines two-dimensional imaging with spectroscopy to simultaneously acquire spatial and spectral information from an object. Numerous studies have shown that this method is capable of accurately estimating internal fruit parameters in a non-destructive manner. The objective of this research was to develop a fast and reliable method for the non-destructive estimation of pH in two plum cultivars using HSI and machine learning algorithms such as Partial Least Squares Regression (PLSR) and Artificial Neural Networks (ANN). This study aims to overcome the limitations of conventional methods by leveraging the power of advanced imaging and computational techniques, providing a sustainable and efficient solution for the fruit industry.
Materials and Methods
In this study, 80 samples from each of the Khormaei and Khoni plum cultivars were used, which were purchased from local orchards. The samples were uniform in size, shape, and colour and were free from any physical damage. Hyperspectral images of the samples were acquired using a rotating hyperspectral imaging system in the range of 418 to 1072 nm. The pH of each fruit juice sample was measured using a digital pH meter. In the analysis of spectral data, the initial part of the spectrum was first removed due to high noise, and then the remaining data were processed with preprocessing methods such as a Gaussian filter and Multiplicative Scatter Correction (MSC). To select effective wavelengths (EWs), a hybrid approach using a Decision Tree (DT) and five metaheuristic algorithms was employed, with the Particle Swarm Optimisation (PSO) algorithm showing the best performance. Finally, pH modelling was performed on the selected wavelengths using PLSR and ANN. This comprehensive methodology ensures that the models are trained on high-quality data and are optimised for maximum accuracy.
Results and Discussion
Spectral analysis showed that the reflectance spectra of the Khoni and Khormaei plums had a high degree of variation, which is related to the differences in their chemical composition and structure. Descriptive statistics indicated that the average pH of Khormaei plum (3.909) was higher than that of Khoni plum (3.7375), and the pH range of Khoni plum (3.15 to 4.44) was wider than that of Khormaei plum (3.6 to 4.2). The results showed that modelling with ANN on the wavelengths selected by PSO, especially for Khoni plum, significantly increased prediction accuracy. The best ANN model for Khoni plum achieved an R²​ of 0.9834 and an RPD of 8.01, which indicates the outstanding accuracy of this method. For the Khormaei plum, the best ANN model also reached an R²​ of approximately 0.76 and a Ratio of Performance to Deviation (RPD) of 2.12, showing a considerable improvement over the PLSR model. The superior performance of the ANN models can be attributed to their ability to capture complex, non-linear relationships between spectral data and pH values, which linear models like PLSR may miss.
Conclusion
This research successfully demonstrated that hyperspectral imaging, in combination with machine learning algorithms, particularly ANN and PSO, can be an accurate and reliable method for the non-destructive prediction of pH in different plum cultivars. The hybrid approach used in this study, which combined DT for initial feature selection with PSO for optimal wavelength selection, enabled the models to predict pH values with very high accuracy, especially for the Khoni plum cultivar. This method can be used as an efficient tool in post-harvest quality control processes, helping to reduce waste and improve efficiency in the fruit supply chain. This work paves the way for the development of smart grading and sorting systems that can quickly and accurately assess fruit quality, benefiting both producers and consumers.

Abstract The performance of mini hand tractors is crucial for improving productivity and operational efficiency in wetland rice farming. This study aimed to evaluate the effects of plough type, tillage pattern, and operating speed on mini hand tractor performance in the border region of Tarakan, Indonesia. Field experiments were conducted from September 2024 to January 2025 using a factorial design (3×5×2) and quantitative descriptive analysis supported by SPSS Statistics 26 for numerical comparison. Performance indicators included wheel slip (%), field efficiency (%), fuel consumption (L h⁻¹), and engine temperature (°C). Results showed that the rotary plough operating under the central tillage pattern at low speed (1 m s⁻¹) achieved the highest field efficiency (78%) and the lowest fuel consumption (1.306 L h⁻¹). In contrast, the disc plough with the central border pattern at high speed (2.3 m s⁻¹) produced the highest wheel slip (48%) and lowest efficiency (22%), indicating substantial performance losses due to excessive soil–wheel friction. Engine temperature increased proportionally with tractor speed, reaching up to 70 °C during high-speed operations. These findings demonstrate that optimising plough type and tillage pattern selection can enhance tractor efficiency by up to 56%, reduce fuel use by 0.8 L h⁻¹, and improve operational stability in wetland conditions. The study provides practical recommendations for selecting and operating mini hand tractors to enhance energy efficiency and sustainability in wetland mechanisation systems across Southeast Asian border regions.

Abstract Amid escalating pressures on global food systems, driven by resource constraints, climatic variability, and rural labour shortages, agricultural mechanisation has become a strategic lever for enhancing productivity and sustainability. This study develops and applies a system dynamics model to examine the long-term effects of mechanisation on wheat cultivated area and yield in fragmented farming systems. The research begins by constructing a causal loop diagram (CLD) to conceptualise the key feedback structures governing mechanisation dynamics. Building on this framework, a stock-and-flow simulation model is formulated and empirically validated using provincial-level data from Khuzestan, Iran (2011-2022). Validation results demonstrate strong alignment between simulated and observed trends across major indicators, including power availability, mechanisation level, cultivated area, and yield. The model is subsequently used to simulate alternative policy scenarios targeting machinery fleet modernisation, water availability, and precipitation variability. In Scenario 3, a 30% increase in the machinery replacement rate leads to a 7% rise in yield and a 1% expansion in the cultivated area, relative to baseline projections. When mechanisation improvements coincide with enhanced water availability, the marginal impact of mechanisation on land expansion becomes negligible (less than 1% increase), indicating a behavioural shift among farmers toward higher-value crops under favourable hydrological conditions. In contrast, under water-scarce scenarios, wheat area expands by approximately 1-1.5%, while yield improvements remain below 3%, reflecting both the crop’s adaptability and the compensating efficiency gains enabled by mechanisation. These findings underscore the importance of accounting for water–mechanisation interactions in policy design, particularly in arid and semi-arid regions. The model offers a flexible and empirically grounded decision-support tool for policymakers seeking to improve climate resilience, optimise resource use, and foster sustainable intensification in agricultural ecosystems facing structural and environmental challenges.

Abstract Okra (Abelmoschus esculentus L.) slices were dried using vacuum-infrared drying method. A laboratory-scale dryer was designed and fabricated to control the drying parameters and monitor the weight change of samples. The parameters examined included temperature (50, 55, 65, 75, and 80ºC) and absolute pressure (5, 21, 53, 85, and 101 kPa). The response factors were drying time, drying rate, effective diffusivity, specific energy consumption, colour change (ΔE and a/a₀), and rehydration ratio. The results from the response surface methodology indicated that the optimal conditions for minimising drying time and maximising green colour preservation were a pressure of 5 kPa and a temperature of 51ºC. Under these conditions, the corresponding responses of dried okra slices were 173 min for drying time, 0.24 %wb min^-1 for drying rate, 1.44×10^-9 m² s^-1 for effective diffusivity, 32.6 kWh kg^-1 for specific energy consumption, 12.64 for colour change, 0.75 for relative greenness, and 6.8 for rehydration ratio. Additionally, the findings demonstrated that the combination of pressures lower than 20 kPa with infrared drying effectively enhanced the performance factors of the dryer while preserving the colour of dried okra slices.

Abstract This study assesses the capability of Sentinel-2A imagery combined with machine-learning classifiers on the Google Earth Engine platform for accurate orchard mapping in the environmentally stressed Sharviran Plain along the southern margin of Lake Urmia, Iran. Random Forest, Support Vector Machine, and Classification and Regression Trees were applied to multispectral bands and selected spectral and vegetation indices, using 836 systematically collected samples from high-resolution imagery and field surveys for training and validation. The study specifically focuses on orchard mapping, with particular emphasis on reliably distinguishing orchards from other agricultural and non-agricultural land-use classes. Results show that orchards cover approximately 11% of the study area, and that the Random Forest classifier achieved the highest performance (Kappa = 0.84) and the best orchard F1-score, demonstrating superior class-level discrimination. Validation against 2023 ground-based orchard statistics showed a low error margin of 2.2%, confirming the reliability of the approach for operational orchard mapping. These findings confirm that integrating Sentinel-2A imagery with machine-learning classifiers on cloud-based platforms offers a robust, reproducible, and cost-efficient framework for orchard mapping in data-scarce and drought-affected regions. The resulting high-resolution orchard maps can effectively support agricultural planning, irrigation management, and evidence-based policy-making in the Lake Urmia basin. Future work may further improve performance by incorporating multi-temporal imagery, additional ecological and socio-economic variables, and advanced models such as deep learning.

Abstract Introduction
Rosa damascena Mill is a valuable cultivated plant, and for many years, essential oil has been produced from its flowers in Iran. The flower buds are generally dried by spreading them out in the shade or in the sun. Shade drying can lead to prolonged drying times, while sun drying may reduce product quality, affecting colour and essential oil. Therefore, in the current study, Rosa flower buds were dried using different drying processes and periods, and their physicochemical properties and essential oil quality were determined.
Materials and Methods
Rosa flower buds were collected from a farm located in Markazi province, Iran, in late May, 2024. Buds were dried using shade, a cabinet dryer (30 and 40 °C), an indirect solar dryer, a freeze-dryer, and a vacuum-dryer. Colour specifications (Lab) of the inner and outer petals of dried buds were measured by a colorimeter. Titratable acidity was determined by the titration method using 0.1 normal sodium hydroxide solution. Ascorbic acid was also measured by the titration method with 2 and 6 dichlorophenol indophenol. Experiments were carried out in three replications using a completely randomised design, and data were analysed using one-way ANOVA. Afterwards, the means of the data were compared using the Duncan test. The extraction of essential oil was applied by the water distillation method. After determining the essential oil yield, the compounds’ percentages were identified using GC and GC-MS devices.
Results and Discussion
The results revealed that drying in shade took too long (more than 12 days), and the shortest drying time happened in the vacuum-dryer (18 hours). The maximum colour index (L*) in the outer and inner petals of dried buds was observed in the freeze-dryer as 49.42 and 44.94, respectively. The maximum value of the a* index of 25.61 was acquired in the outer petal buds, which were dried at 50 °C in the cabinet dryer. After the fresh buds, the a* index of the inner petal buds was highest in the vacuum dryer at 16.62. The b* index of the outer and inner petal buds dried in the cabinet dryer, solar dryer, shade, and vacuum dryer did not have any significant differences. The maximum titratable acidity value was related to buds dried in the solar dryer (2.3%), and the minimum was observed in the vacuum dryer (1.23%). In the cabinet dryer (40 and 50 °C), ascorbic acid of dried buds had the highest values (1.57 and 1.49 mg per 100 g wet matter) in comparison with the other treatments. The quality of the essential oil extracted from dried buds in shade was similar to that of fresh buds. After the shade drying method, the best quality of essential oil was observed in buds dried in the cabinet dryer at 40 °C (30.4%).
Conclusion
Based on the results, applying vacuum-drying considerably shortened the drying period in comparison with shade drying. Lightness (L) is the most important specification that had the maximum value in outer and inner petals of buds dried in the freeze-dryer, and the least in the solar dryer. There were no significant differences between the lightness of inner petals of buds dried in shade and the two treatments of the cabinet dryer. Drying caused an increase in titratable acidity and a decrease in ascorbic acid of the flower buds. The maximum titratable acidity was depicted in buds dried by an indirect solar dryer, and the minimum was in the vacuum dryer. The flower buds dried in the cabinet dryer contained significantly higher levels of ascorbic acid compared to other drying methods. Cabinet drying at 50 °C yielded the highest amount of essential oil. The most aroma compounds and the lowest waxy compounds were observed in fresh buds, buds dried in shade, and buds which dried at 40 °C in the cabinet dryer, respectively.

Abstract A study was carried out to investigate a motorised, integrated cassava tuber slicing and chopping machine. The integrated machine was investigated in terms of key performance metrics at three levels of speeds (950, 1150, and 1350 rpm for chopping; 300, 450, and 600 rpm for slicing) and two levels of feed rates (10 and 15 kg min^-1 for chopping; 5 and 10 kg min^-1 for slicing). According to the investigation findings, the maximum chopping capacity of 229.7 kg h^-1 was recorded at an operating speed of 1350 rpm and a feeding rate of 10 kg min^-1. The maximum chopping efficiency of 82.1% was obtained at an operating speed of 1350 rpm and a feeding rate of 15 kg min^-1, whereas the minimum mechanical loss of 9.06% was found at an operating speed of 1350 rpm and a feed rate of 15 kg min^-1. Based on the investigation results, at 600 rpm rotational speed and 5 kg min^-1 feed rate, the greatest slicer capacity of 114.8 kg h^-1 was noted. At 600 rpm rotational speed and 10 kg min^-1 feed rate, the greatest slicer efficiency of 71.6% was obtained. At this speed and feeding rate, the smallest loss of 11.06% was observed. Due to the low-key performance criteria recorded during the research, optimisation of the drum and hopper of the chopping unit, as well as the hopper and blade geometry of the slicing unit, is recommended.

Abstract The red palm weevil (Rhynchophorus ferrugineus) is a destructive pest of date palms whose concealed larval feeding makes early detection extremely difficult. To capture weak chewing signals under natural orchard conditions, a portable bioacoustic sensing unit was developed using a MAX9814 electret microphone integrated with an STM32F103C8T6 12-bit data acquisition module operating at 16 kHz. The compact design, isolated power, and vibration-damping mount enabled reliable, non-invasive recording in field conditions. Acoustic emissions from infested palms were analysed in both time and frequency domains using six key spectral features: mean and median frequency, band power, occupied and power bandwidth, and peak location, across five frame durations. Statistical analysis revealed how segmentation scale affects signal stability, impulsiveness, and redundancy among features. A structured spectral atlas was then established, consolidating scattered acoustic descriptors into a unified representation. Dominant larval activity occurred below 7 kHz, with mean and median frequencies between 2.1 and 3.8 kHz (r > 0.85), and Band power values ranged from approximately 1.7×10⁻⁵ to 2.6×10⁻⁵ (relative linear scale) and increased during periods of intense chewing. The occupied bandwidth (1.5 to 2.8 kHz) narrowed during intense chewing, confirming spectral consistency and diagnostic value. Unlike prior studies that primarily report classification performance, this work introduces a structured spectral atlas that quantitatively characterises the stability, variability, and interrelationships of key acoustic features across multiple time scales. This descriptive baseline provides a foundation for informed feature selection, sensor bandwidth design, and the development of future embedded, non-invasive acoustic systems for early RPW infestation monitoring.

Abstract Introduction
Improving the sustainability of agricultural systems requires the optimisation of inputs use, especially chemical fertilisers and pesticides, which are major contributors to energy consumption and environmental degradation. Variable Rate Technology (VRT), as a precision agriculture strategy, allows site-specific management of inputs based on spatial variability in soil fertility, weed distribution, and crop requirements. Although VRT has shown promise in enhancing resource-use efficiency, its integrated effects on energy indicators and environmental burdens in wheat production under irrigated conditions remain insufficiently investigated. This study assessed the impact of VRT on energy performance and environmental emissions in comparison with conventional uniform application, using a real six-hectare winter wheat field in Karaj, Iran.
Materials and Methods
A comprehensive field-scale simulation of VRT was conducted for nitrogen, phosphorus, potassium fertilisers, herbicides, and insecticides. Spatial maps of soil fertility and weed distribution were generated using UAV-based remote sensing and ground sampling. Two scenarios were examined: (1) VRT-based variable application of chemical inputs and (2) conventional uniform application. Energy inputs and other outputs were calculated based on standard coefficients and categorised as direct, indirect, renewable, and non-renewable. Environmental impacts, including Global Warming Potential (GWP) and pollutant emissions to air, water, and soil, were quantified using the ReCiPe 2016 Midpoint (H) method. All results were compared for the production of 34,800 kg of wheat.
Results and Discussion
Energy Indicators
The total energy input under VRT (131,631.57 MJ) was lower than that of consumed under conventional management (160,318.55 MJ). Direct and indirect energy uses declined by 12.64 MJ and 19.31 MJ, respectively, in the VRT system. VRT improved all energy indicators i.e., energy ratio increased to 6.82 (21.79% higher than the conventional method), energy productivity rose to 0.264 kg MJ⁻¹, and energy intensity decreased to 3.78 MJ kg⁻¹. Net energy under VRT reached 765,705.76 MJ, exceeding the conventional value. Major reductions were attributed to substantial decreases in herbicide use (80.40%) and potassium fertiliser (77%), driven by UAV-derived weed distribution maps and soil fertility maps.
Environmental Impacts
The GWP of the VRT scenario was 17,691.21 kg CO₂-eq, representing approximately a 20% reduction compared to the 22,202.74 kg CO₂-eq emitted under conventional application. Nearly half of the GWP originated from direct field emissions, followed by nitrogen fertiliser use. Optimisation of nitrogen rates and reduced field-level emissions were the primary contributors to the decrease. Pollutant emissions to the atmosphere also declined significantly: CO₂ by 16.2%, N₂O by 21.9%, and NH₃ by 22.5%. Waterborne pollutants were reduced, with nitrate declining by 22.4% and phosphorus by 38.9%. Heavy metal emissions to soil also decreased, with Pb reduced by 22.4% and Zn by 35.0%, while elements with naturally low accumulation (Fe, B, Mn, Mo) remained unchanged. These improvements align with previous international findings demonstrating the effectiveness of UAV-assisted VRT in reducing chemical inputs, enhancing energy efficiency, and minimising environmental pollution across diverse cropping systems.
Conclusion
The results demonstrate that implementing VRT for applaying chemical inputs in wheat production substantially reduces both energy consumption and environmental impacts. Compared with the conventional uniform method, VRT improved all energy indicators, decreased total energy inputs, and increased net energy output. Environmentally, VRT reduced GWP by roughly 20%, lowered key atmospheric pollutants, and substantially decreased nutrient leaching and heavy metal accumulation in soil. Overall, VRT proves to be a highly effective strategy for achieving sustainable wheat production through optimised input management, enhanced energy efficiency, and minimised ecological burdens.
Acknowledgement 
The authors gratefully acknowledge the support of the agricultural research team and field specialists involved in data collection, UAV operations, and soil and crop analyses throughout the study.

Abstract Accurate pre-harvest yield estimation is essential in precision horticulture, enabling informed decisions in labour allocation, logistics, and market forecasting. This study presents an integrated computer vision framework based on the YOLOv12 architecture for accurate detection and quantification of persimmon (Diospyros kaki) fruits under real orchard conditions. Multi-scale feature extraction was combined with lightweight regression modelling to predict individual fruit weights directly from RGB imagery. A dedicated dataset of 2,118 annotated images was compiled to capture variations in canopy density and illumination, ensuring robust performance under natural occlusions. After fine-tuning five YOLOv12 variants, detection precision exceeded 0.92 mAP@0.5, with the YOLOv12x configuration achieving the highest accuracy of 0.945. However, the YOLOv12n variant provided the best trade-off between accuracy (0.925 mAP@0.5) and efficiency (5.7 ms inference, >100 FPS), making it optimal for IoT-based real-time deployment in smart orchard environments. Yield estimation was derived by mapping ellipsoidal volume approximations to actual fruit weights using linear regression, achieving a mean absolute error of 6.2 g per fruit and an average tree-level deviation below 5%. The framework maintained real-time inference speeds on edge devices, confirming its suitability for practical field applications. The results demonstrate its reliability for data-driven yield forecasting and highlight its potential integration into IoT-enabled harvesting systems to minimise post-harvest losses through predictive resource allocation.

Abstract Rice husk is a widely available agricultural residue used for energy generation in many developing countries. Its gasification in micro-gasifier stoves offers a practical method for clean energy production. However, the unique shape and bulk density of rice husk cause fuel bridging and poor flow, leading to incomplete gasification unless sufficient aeration is provided. In this study, design modifications were applied to an existing micro-gasifier stove, and a forced-aeration system was integrated to ensure adequate oxygen supply during combustion. A full factorial experiment was conducted to evaluate stove performance, with injection air velocity and fuel mass selected as experimental factors. Using a 3.97-litre micro-gasifier stove, three air velocities (0.6, 0.7, and 0.8 m s^-1) and three fuel levels (200, 300, and 400 g) were tested during the cold-start phase following the water boiling test protocol. Results were statistically analysed using factorial analysis within a completely randomised design. The findings showed that both air velocity and fuel mass factors significantly affect stove performance metrics (p ≤ 0.05), including thermal power, thermal efficiency, fuel consumption rate, and boiling time. The highest thermal efficiency (51.7%) was achieved with 200 g of fuel at an air velocity of 0.6 m s^-1. The maximum thermal power (5420 W) occurred at an air velocity of 0.8 m s^-1.

Abstract Fossil fuel limitations and environmental concerns have increased interest in renewable energy sources like solar power for agricultural applications. This study presents the development and evaluation of a photovoltaic water pumping system equipped with a single-axis solar tracker, designed to automate irrigation in gardens and fields lacking access to the electrical grid. The system integrates solar energy absorption and storage, a solar tracker, and automatic irrigation units. Solar energy captured by the panel is transferred to a battery via a charge controller, then converted through a voltage converter and motor driver to operate the water pump. Experimental results indicate that the system’s energy intake peaks between 10 a.m. and 2 p.m. Under sunny conditions with an active tracker, the system stored 53.58 watts in the battery, compared to 43.4 watts with an inactive tracker, and 35.3 watts during cloudy weather. The study also found that the orientation of the solar panel relative to the sun and the use of the solar tracker significantly influenced energy collection, with statistical significance at the 5% level. Additionally, the type of soil moisture sensor impacted system performance, with a disturbance matrix demonstrating 100% irrigation accuracy. Overall, the solar tracker proved effective in sunny conditions, enhancing energy collection and system efficiency. The findings support the adoption of such systems for automatic irrigation, especially in remote or off-grid locations, contributing to sustainable agricultural practices and reducing reliance on fossil fuels. The integration of tracking technology and optimised sensor use can significantly improve the reliability and efficiency of solar-powered irrigation systems, making them a viable solution for modern agriculture.

Abstract Reliable cow detection in barn environments remains challenging due to visual occlusion, overlapping animals, and cluttered backgrounds. To improve accuracy and real-time performance under such conditions, this study presents YOLO11-DML, an enhanced detection framework built upon YOLO11. To address the decline in recognition accuracy caused by cow overlaps, occlusions, and background interference, this paper has implemented optimisations in three aspects: feature extraction, attention mechanism, and lightweight detection head design: The C3K2-DIMB module is introduced into the Backbone and Neck, enhancing multi-scale feature modelling capabilities through dynamic convolution kernel weights; The mixed local channel attention (MLCA) hybrid local-channel attention mechanism is embedded at the end of the Backbone as an auxiliary feature enhancement module to refine hierarchical feature representations; A lightweight shared convolutional detection head (LSCDH) is designed for the detection head, effectively reducing parameter count and computational overhead while maintaining detection accuracy. Experiments conducted on the CBVD-5 and Dairy Cow datasets show that YOLO11-DML achieves a precision (P) of 92.57%, an F1-score of 89.07%, an mAP@0.5 of 93.11%, an mAP@0.5:0.95 of 59.37%, an inference speed of 105.79 FPS, a parameter count of 2.16M, 5.1 GFLOPs of floating-point operations, and a model size of only 4.5 MB. The research demonstrates that YOLO11-DML achieves high precision while maintaining a lightweight design and real-time performance, providing a feasible solution for multi-object dairy cow detection and behaviour monitoring in smart farms.

Abstract Introduction
The electronic nose (e-nose) system analyses the volatile compounds in products by mimicking the human olfactory system and is capable of providing both chemical and sensory information. One of the most important applications of the electronic nose is gas sensors widely used in various industries, including construction, chemical and petrochemical, environmental monitoring, medical and pharmaceutical, agricultural, and food industries, as well as in many other processes where gas monitoring and analysis are essential. Due to the increasing cost of experimental testing, computational fluid dynamics (CFD) can be employed to investigate the effects of key factors in the electronic nose. CFD is a branch of numerical methods used to solve the governing equations describing various flow phenomena. In CFD, different methods and algorithms are utilised to obtain solutions; however, in all cases, the problem domain is discretised into a large number of small elements, and the governing equations are solved for each element. A review of previous studies indicates that simulation processes aimed at achieving reliable results in this field have received considerable attention, and the data obtained from these simulations can be highly accurate and efficient. Moreover, the sensor chamber plays an important role in enhancing the performance, stability, and sensitivity of an electronic nose. Therefore, four different configurations of 3D sensing chambers were simulated using ANSYS FLUENT software, examining both air and CO₂ as fluids. Numerical simulations were carried out to investigate the gas flow behaviour inside these four chambers and specify the optimal chamber design with the best stability time.
Materials and Methods
In the present study, four chamber geometries namely cylindrical, pyramidal, conical, and hemispherical, were designed using CATIA software. Subsequently, three-dimensional simulations were performed using Ansys Fluent software. To predict the fluid flow behaviour, the continuity equation and the Navier–Stokes equations were employed. The boundary conditions were identical for all geometries, and given the equal inlet and outlet cross-sectional areas across all configurations, the only difference among the models lies in their overall geometric structure. The electronic nose chambers were tested with a laminar flow and the SIMPLEC method.
Results and Discussion
The 3-Dimensional simulations were conducted for the four geometries under two fluid injections, air and carbon dioxide, while maintaining identical boundary conditions. To investigate the fluid behaviour inside the geometries, flow field contours were presented at 25 s, 50 s, 75 s, and under approximate stability conditions when the contours became nearly time-invariant. In the air injection case, the fluid inside the hemispherical geometry reached steady-state conditions by 25 s, faster than in the other geometries, and maintained a similar flow pattern at later times. Based on the simulation results, the hemispherical geometry exhibited the best overall performance. This favourable behaviour can be attributed to the geometric dimensions of the hemisphere in the sensor surface region. In contrast, the behaviour of carbon dioxide differed significantly from that of air. Under carbon dioxide injection, similar to the air injection case, the fluid in the hemispherical geometry reached steady-state conditions by 25 s and fully covered the outlet region. In this geometry, the flow attained a nearly uniform distribution at 25 s, and the fluid fully contacted the sensor surface. Also, results showed that small vortices produced near the sensor surface can improve the stability time, and they can help to bring the fluid to the surface faster.
Conclusion
In the present study, four geometries were investigated under two injection conditions: air and carbon dioxide. The evaluations were conducted to identify the optimal geometry in terms of fluid flow behaviour, sensor surface coverage, and flow stability. The results demonstrated that the hemispherical geometry exhibited the best performance under both air and carbon dioxide injection conditions. This indicates that providing a larger fluid–sensor contact area along with a balanced geometric configuration can lead to improved fluid behaviour and overall system performance.

Abstract Oil palm supports Indonesia’s rural economy, yet field maintenance operations remain labour-intensive and measurement sparse. This engineering science driven review synthesises 20 studies published during 2015–2025 on weed control, fertiliser application, pest and disease management, pruning/canopy work, and supporting soil/residue operations in Indonesian oil palm. To handle heterogeneous reporting, performance indicators were classified as directly reported metrics, derived metrics, proxy indicators, or not reported; no missing values were imputed for CV, deposition, drift, or droplet spectrum descriptors. Across the available evidence, electric/motorised sprayers, CDA systems, and UAV spraying generally reduced time and/or cost relative to manual knapsack practice, while tractor towed spreaders, pneumatic applicators, and subsurface placement concepts improved dosing and targeting in fertiliser operations. However, only a minority of studies reported standardised engineering metrics such as CV, VMD, on target deposition, drift, or operator exposure. The review provides a structured engineering comparison rather than a formal meta-analysis and identifies priority research needs: standard test protocols, comparable benchmark reporting, and modular multifunction platforms integrated with sensing, decision support, and service-based deployment models.

Abstract Accurate and timely detection of maize (Zea mays L.) foliar diseases is essential for improving crop productivity and ensuring food security. This study evaluates the performance of state-of-the-art deep learning models for automated maize leaf disease classification using a real-field dataset collected from agricultural farms in Kandahar Province, Afghanistan. The dataset was acquired under natural field conditions, preserving complex backgrounds, variable illumination, and diverse leaf orientations to reflect practical farming environments. Multiple advanced transformer-based deep learning architectures, including Vision Transformer (ViT), and hybrid Multi-axis Vision Transformer (MaxViT-tiny, MaxViT-small, and MaxViT-base) were investigated and compared with EfficientNet (B5, B6, and B7) models. Experimental results demonstrate that modern CNN-based and transformer-based architectures achieve up to 97% accuracy, despite challenging real-world conditions. Among the evaluated models, EfficientNet-B7, ViT, and MaxViT-Tiny attained the highest accuracy of 97%, with MaxViT-Tiny recording the highest precision (0.9741), indicating exceptional class discrimination, particularly for challenging Spot and Blight classes under real-field variability. Minor misclassifications were primarily observed between visually similar disease classes, while overall precision and recall remained consistently high. The findings confirm the effectiveness of hybrid and attention-based architectures for real-world maize disease detection and demonstrate their feasibility for real-world agricultural applications. By leveraging authentic field data, this study provides practical and ecologically valid insights that support early disease diagnosis, reduced manual inspection, and improved sustainable crop management.

Abstract Improving wheat productivity is essential for maintaining food security in the coming years. A major challenge to wheat production is the presence of weeds, which can greatly reduce its yield. In this context, precision weed management based on weed-crop classification can be a key factor for achieving sustainable wheat production. To accomplish this goal, in the present study, we utilised colour images and deep learning techniques to distinguish winter wheat from four prevalent weeds, creating five distinct classes. Four deep learning networks–Xception, EfficientNetB0, VGG19, and InceptionResNetV2–were evaluated to serve as backbone networks to fulfil the classification requirements. These networks were pre-trained on ImageNet using transfer learning, then enhanced with additional layers to improve performance on our dataset. The improved InceptionResNetV2 model demonstrated the highest performance among the four models, achieving an accuracy of 98.17% and a loss of 3.19% on the test data. Nevertheless, all models demonstrated excellent performance in distinguishing plant classes, achieving weighted average F1-scores of 97%, 86%, 94%, and 98% for the improved models based on Xception, EfficientNetB0, VGG19, and InceptionResNetV2, respectively. Additionally, we analysed fifteen scenarios of weed presence in winter wheat fields, focusing on various weed types studied, to propose effective weed management strategies utilising the four models. The research findings provide a foundation for developing mobile apps or robotic weeders that facilitate site-specific weed management. This approach aims to reduce herbicide use and environmental impact while also improving wheat yield and quality.

Abstract Introduction
Ensuring the authenticity and safety of almonds is a critical food safety challenge, primarily due to the presence of amygdalin in bitter varieties, a cyanogenic glycoside that can release toxic hydrogen cyanide upon hydrolysis. Traditional detection methods, such as chromatography, are often destructive, time-consuming, and unsuitable for industrial-scale applications. While the current laboratory-scale analysis relies on a sample preparation method (KBr pellet) that is inherently destructive, the ultimate goal of this research is to develop a non-destructive, automated sorting system suitable for industrial implementation. This can be achieved through the use of alternative FTIR techniques, such as Attenuated Total Reflectance (ATR)-FTIR, which allows for direct analysis of the almond shell surface without the need for sample preparation. This study addresses the need for rapid, non-destructive, and automated sorting by developing an integrated framework that combines Fourier-transform infrared (FTIR) spectroscopy of almond shells with machine learning and mechatronic automation. The research specifically focuses on exploiting the rich phenolic content of almond shells as a novel, low-cost substrate for reliable classification.
Materials and Methods
A set of 200 almonds (100 bitter, 100 sweet) was collected. Their shells were separated, vacuum-dried, ground into powder, and pressed into potassium bromide (KBr) pellets for analysis. FTIR spectra were recorded in the range of 400–4000 cm⁻¹. The raw spectral data underwent preprocessing using first and second Savitzky–Golay derivatives, Standard Normal Variate (SNV), and Multiplicative Scatter Correction (MSC). Four supervised learning algorithms, Support Vector Machine (SVM), Random Forest (RF), Multi-Layer Perceptron (MLP), and an Autoencoder-MLP hybrid, were trained and compared, using a 70-30 train-test split with fivefold cross-validation. The best-performing model's output was integrated with a custom-built laboratory-scale mechatronic sorting system. This system featured a conveyor belt, a microcontroller-based control board, and a mechanical deflection mechanism to physically separate the almonds based on the classification decision.
Results and Discussion
Among the tested models, the MLP network achieved the highest classification performance for almond shells, with an accuracy of 95.5% and an Area Under the Curve (AUC) of 0.984. Statistical analysis via the McNemar test confirmed its significant superiority over the best classical model (RF, p < 0.01). This superior performance is attributed to the MLP's ability to model the complex, non-linear relationships within the high-dimensional FTIR spectral data, which traditional linear models like SVM (accuracy 82.5%) failed to capture effectively. Feature importance analysis revealed three key discriminating spectral bands: 1030 cm⁻¹ (associated with phenolic C–O stretching), 1740 cm⁻¹ (related to carbonyl C=O stretching), and 2920 cm⁻¹ (linked to aliphatic C–H stretching). These bands were more pronounced in bitter almond shells, corresponding to their higher phenolic and lipid content, including amygdalin derivatives. Dimensionality reduction visualisations using t-SNE and UMAP on the latent features extracted by the Autoencoder-MLP model further corroborated the superior class separability achieved by deep learning approaches compared to linear methods like PCA. When the MLP model's decisions were deployed on the mechatronic sorter, the system achieved an average physical sorting accuracy of 99.5% across consecutive tests with varying sample compositions. Error analysis indicated that the primary sources of infrequent mis-sorting were related to mechanical synchronisation and sample positioning on the conveyor, rather than errors in the MLP classification algorithm itself. The throughput was measured at 30–35 almonds per minute, demonstrating practical potential for medium-scale processing. This performance aligns with or surpasses the accuracy ranges reported in recent studies utilising HSI or ATR-FTIR for almond discrimination, while uniquely adding the critical step of real-time physical separation.
Conclusion
This study successfully demonstrates the feasibility of a non-destructive, integrated system for almond authentication and sorting. The integration of shell-based FTIR spectroscopy with an MLP classifier yields superior analytical accuracy. Crucially, the direct integration of this classification into an automated mechatronic sorter bridges the gap between laboratory detection and industrial application. The approach offers a scalable, cost-effective, and non-destructive solution for enhancing food safety in almond processing by enabling the real-time removal of toxic bitter almonds from production lines.
Acknowledgement
The authors gratefully acknowledge the staff of the Chemistry Laboratory of Shahid Bahonar University of Kerman for providing access to FTIR equipment and technical support during the spectral acquisition and analysis process.

Abstract Introduction
The qualitative grading of raisins, as an important agricultural commodity, plays a critical role in determining market value, pricing strategies, and competitiveness in both domestic and international markets. Conventional intelligent grading approaches have largely relied on manual feature extraction and complex image preprocessing techniques, which substantially increase computational cost and system complexity. Consequently, there is an increasing demand for automated, accurate, and non-destructive grading methods based on deep learning technologies, particularly for golden Sultana raisins.
The primary objective of this study is to develop a deep convolutional neural network (DCNN) capable of grading and evaluating golden Sultana raisins without requiring explicit image preprocessing, while simultaneously reducing processing time and computational overhead. Unlike previous studies that mainly focused on computational limitations or restricted classification schemes, the proposed model automatically extracts discriminative visual features; including texture, shape, and colour to accurately assess raisin quality and size under varying imaging conditions. This capability enhances robustness against variations in illumination, imaging angle, and background, thereby eliminating the need for complex preprocessing pipelines or manual parameter tuning.
Materials and Methods
In this study, a dedicated deep convolutional neural network, referred to as Raisin_DCNN, was specifically designed for grading golden Sultana raisin grains. The dataset consisted of 3,320 images representing four distinct quality grades. All images were acquired using a flatbed scanner under controlled lighting conditions to ensure consistency and uniform image quality.
To increase dataset diversity and reduce the risk of overfitting, data augmentation techniques were applied. These techniques included random horizontal and vertical flipping, cropping, shifting, zooming, and rotation. Notably, the proposed model was trained directly on the augmented images without applying complex preprocessing operations or manual feature extraction methods.
The Raisin_DCNN architecture was purpose-built to effectively extract visual features from each raisin. It comprises multiple convolutional layers with small kernel sizes, followed by MaxPooling layers for spatial dimensionality reduction, and fully connected layers at the final stage. ReLU activation functions were employed to facilitate the learning of nonlinear feature representations, while a Softmax function was used in the output layer to perform four-class classification. The architecture was carefully optimised to achieve an appropriate balance between classification accuracy and computational efficiency.
The dataset was divided into training, validation, and test subsets. Network training was performed using the Adam optimisation algorithm in conjunction with the categorical cross-entropy loss function. Learning rate scheduling and early stopping strategies were implemented to improve convergence and prevent overfitting. Hyperparameter tuning was conducted through iterative experimentation based on validation performance.
For comprehensive performance evaluation, the proposed Raisin_DCNN model was compared with transfer learning approaches based on pre-trained InceptionV3 and InceptionResNetV2 architectures, as well as classical machine learning methods employing histogram-oriented gradients (HOG) and scale invariant feature transform (SIFT) feature extraction combined with support vector machine (SVM), k-nearest neighbours (KNN), and logistic regression (LR) classifiers.
Results and Discussion
Model performance was assessed using accuracy, precision, recall, and F1-score metrics. Experimental results demonstrated that the Raisin_DCNN model achieved an overall classification accuracy of 96%, outperforming both the transfer learning models and classical feature-based approaches. This superior performance highlights the effectiveness of the proposed network in automatically learning discriminative features directly from images of raisins. Furthermore, the elimination of complex preprocessing stages significantly reduced computational cost, thereby improving system efficiency and practical applicability.
Conclusion
The results of this study confirm that a dedicated deep convolutional neural network can provide an accurate, robust, and reliable solution for the automatic grading of golden Sultana raisins. The proposed Raisin_DCNN model demonstrates strong potential for real-world deployment in industrial grading systems and offers an effective approach for enhancing quality control processes in agricultural product inspection.
Acknowledgements
The authors sincerely thank all individuals and research groups who contributed to this work and provided technical assistance throughout the study. The authors would like to thank Department of Bio-System Mechanical Engineering, Islamic Azad University of Bonab Branch for their support and participation in this study. The authors also gratefully acknowledge the anonymous reviewers for their insightful comments and constructive suggestions, which significantly improved the quality of this manuscript.

Abstract Mechanisation of smallholder farming faces significant challenges, including high machinery costs, inefficient energy utilisation, and the lack of predictive design methodologies for multifunctional agricultural equipment. This study presents a low-cost modular multifunctional agricultural machine for smallholder applications, integrating soil-tool interaction modelling, nonlinear traction-slip analysis, coupled draft-power-torque relationships, structural stress evaluation, and lifecycle economic assessment within a unified analytical framework. Experimental validation conducted under three representative soil cohesion conditions (n = 36) demonstrated good agreement between analytical predictions and measured performance, with deviations of 8.7%, 9.2%, and 7.5% for draft force, torque, and structural stress, respectively. The proposed system exhibited energy consumption of 4.8–7.9 kWh ha^-1, achieved a 22–26% reduction in operating costs compared with commercially available petrol-operated smallholder tillage units of comparable working width and field capacity, and yielded an estimated payback period of 1.8–2.3 years. The incorporation of traction analysis accounting for wheel slip improved the prediction of energy requirements while maintaining seed spacing uniformity above 87%. Soil cohesion, penetration depth, and slip ratio were identified as the dominant parameters influencing power demand and structural loading. The proposed framework demonstrated improved energy efficiency, operational stability, and economic feasibility under the investigated operating conditions and shows potential for broader smallholder mechanisation applications.

Abstract The ecological footprint reflects human pressure on natural resources and environmental biocapacity and is widely used to assess agricultural sustainability. This study examined the effect of preceding crops (wheat, barley, and canola) on the environmental and economic performance of mung bean production. Data were collected via farmer questionnaires and interviews. Environmental indicators [ecological footprint (EF), biocapacity (BC), ecological balance (EB)] and economic indicators [gross margin, benefit–cost ratio (BCR)] were calculated. Results showed that the preceding crop significantly affected most indicators (p < 0.05); canola differed from wheat and barley, while wheat and barley did not differ significantly. Environmentally, mung bean after canola had the lowest total EF (1.055 gha ha⁻¹) and highest relative ecological efficiency (0.315), but the lowest BC (1.541 gha ha⁻¹). Mung bean after wheat exhibited the highest EF (1.777 gha ha⁻¹) and the highest BC (2.277 gha ha⁻¹). Although EB was positive for all rotations (0.482–0.500 gha ha⁻¹), the Ecological Footprint Index (EFI) fell within the 'weak sustainability' category (0.216–0.315), and the ratio of overexploitation footprint to input footprint (EF_ovp/EF_inp) ranged from 4.30 to 8.59, indicating that overexploitation pressure overwhelmingly dominates input‑driven pressure. Economically, wheat achieved the highest gross margin ($ 1,887 ha⁻¹) and BCR (2.844), while canola gave the lowest ($ 1,032 ha⁻¹; 1.479). A composite index with adjustable ecological‑economic weights revealed a trade‑off threshold at w≈0.55: canola is superior under ecological priority, wheat under economic priority. Given local biocapacity, selecting wheat as the preceding crop offers the most balanced combination of EF management and profitability for mung bean production in this region.

Abstract Introduction
The increasing demand for sustainable and renewable energy sources has intensified research on bioenergy production from agricultural wastes. Anaerobic digestion is a widely applied biological process for converting organic residues into valuable gaseous fuels such as biohydrogen and biomethane. However, the efficiency of this process is often limited by the complex structure of lignocellulosic biomass and the slow hydrolysis step, which acts as a rate‑limiting factor. Therefore, various pretreatment techniques have been proposed to enhance substrate biodegradability and improve gas yields. Among physical pretreatment methods, ultrasonic pretreatment has attracted significant attention due to its ability to disrupt cell walls, reduce particle size, and enhance solubilisation of organic matter through cavitation effects. Despite numerous studies on biogas enhancement using ultrasonic pretreatment, limited research has simultaneously investigated its effect on biohydrogen production, gas composition (H₂, CH₄, CO, and H₂S), and the associated metabolic pathways for different agricultural residues. Accordingly, the main objective of this study was to evaluate the impact of ultrasonic pretreatment on the anaerobic digestion performance of selected agricultural wastes, including corn residues, potato waste, and banana waste. In addition to gas production performance, particular emphasis was placed on analysing changes in metabolic pathways and alcohol formation to better understand the mechanisms governing hydrogen and methane generation.
Materials and Methods
In this study three types of agricultural wastes, namely corn residues, potato waste, and banana waste, were used as feedstocks. The substrates were mixed with animal manure and water to provide appropriate microbial inoculation and moisture content. The prepared mixtures were mechanically stirred at 500 rpm for 10 minutes to ensure homogeneity. Ultrasonic pretreatment was applied using an ultrasonic device operating at 300 W for 5 minutes. Both pretreated and untreated samples were then subjected to anaerobic digestion under controlled conditions. Total solids (TS) and volatile solids (VS) were measured to characterise the substrates before digestion. During the anaerobic digestion process, the composition of the produced gases, including hydrogen (H₂), methane (CH₄), carbon monoxide (CO), and hydrogen sulfide (H₂S), was monitored. In addition, total alcohol concentration (ALC) was measured to assess shifts in fermentation pathways. The obtained data were analysed to compare the performance of ultrasonic pretreatment across different feedstocks and to evaluate its influence on metabolic reactions.
Results and Discussion
The results demonstrated that the effect of ultrasonic pretreatment on gas production strongly depended on the type of agricultural waste. For corn residues, ultrasonic pretreatment significantly enhanced biohydrogen production, increasing hydrogen concentration from approximately 2,585 ppm to 3,900 ppm. Methane production also showed a moderate increase, rising from about 104,000 ppm to 107,000 ppm. These improvements can be attributed to enhanced solubilisation of organic matter and improved accessibility of fermentable substrates. In contrast, potato waste exhibited decreased hydrogen and methane production following ultrasonic pretreatment. This behaviour suggests that excessive disruption of the substrate structure may have promoted alternative metabolic pathways unfavourable for gas generation. Banana waste showed a substantial percentage increase in hydrogen production after ultrasonic pretreatment, although its effect on methane production was less pronounced. Across all substrates, ultrasonic pretreatment led to an increase in carbon monoxide concentration and a noticeable reduction in hydrogen sulfide, which is considered beneficial due to the corrosive nature of H₂S. Metabolic pathway analysis revealed that ultrasonic pretreatment, particularly in banana and corn wastes, promoted pathways leading to alcohol production, such as ethanol and methanol formation. This shift explains the observed reduction or stagnation in hydrogen and methane production during later stages of digestion, as substrates were partially diverted toward solventogenic reactions.
Conclusion
The results of this study indicate that ultrasonic pretreatment can be an effective approach for improving the performance of anaerobic digestion; however, its effectiveness strongly depends on the type of substrate used. Among the investigated agricultural residues, corn waste demonstrated the most favourable response to ultrasonic pretreatment, showing improvements in both hydrogen and methane production. Banana peel also exhibited a noticeable increase in hydrogen generation after pretreatment, whereas potato waste showed a less favourable response and did not benefit significantly from the ultrasonic treatment. In addition, the variations observed in gas composition and alcohol production suggest that changes in microbial metabolic pathways play an important role in determining the outcomes of pretreatment processes. Overall, these findings suggest that ultrasonic pretreatment has considerable potential for enhancing bioenergy recovery from agricultural wastes, provided that pretreatment conditions are carefully optimised according to the characteristics of each substrate.

Abstract Olive harvesting is one of the most labour-intensive operations in olive production, particularly in high-density orchards, where technology selection strongly affects productivity and sustainability. This study combines quantitative field experiments with a sustainability-oriented multi-criteria decision-making framework to evaluate three harvesting methods including hand rake, branch shaker, and over-the-row combine harvester, for Arbequina and Koroneiki cultivars in Iran. A factorial RCBD was implemented to measure harvesting loss, harvesting efficiency, harvesting rate, oil content, and chemical quality indices. Results showed significant differences among methods in harvesting loss, efficiency, and rate (P < 0.01). Hand rake achieved the highest harvesting percentage (100%) but also the highest fruit loss (3.33%). Branch shaker recorded the lowest fruit loss (1.12%) but only 53.8% efficiency. Combine harvester provided the highest harvesting rate (4429–5158 kg h⁻¹) and the greatest economic return, with benefit–cost ratios of 1.297 for Arbequina and 1.375 for Koroneiki. To integrate technical, economic, social, and environmental indicators, fuzzy AHP was used to compute criterion weights, and Grey Relational Analysis (GRA) was applied to rank alternatives. Combine harvester obtained the highest GRA score (0.812), followed by the shaker (0.774) and the hand rake (0.682). A comprehensive sensitivity analysis was conducted using ten weighting scenarios and four multi-criteria decision-making methods (GRA, TOPSIS, VIKOR, and ELECTRE), which confirmed the robustness of the rankings across different policy priorities. Also, scenario-based sensitivity analysis demonstrated that combine harvesters consistently ranked first under economic and technical priorities, while shakers became the preferred option in sustainability-oriented scenarios emphasising employment and reduced non-renewable energy use. These results confirm that no single harvesting method dominates across all performance dimensions. Proposed integrated framework provides a robust, evidence-based decision support tool for selecting olive harvesting technologies that balance operational efficiency, profitability, and long-term sustainability.

Abstract This study investigates the effects of high hydrostatic pressure (HHP) processing applying at 600 MPa for 10 min on pH, total soluble solids, total acidity, total phenolic content, vitamin C, pectin methyl esterase activity, and microbial load of lime juice (Citrus Latifolia, Persian variety) immediately after treatment and during 45 days of storage at 20°C in comparison with conventional thermal pasteurisation at 72°C for 20 s. HHP treatment decreased pH and °Brix of lime juice, while thermal treatment had no effect on the mentioned parameters. The influence of HHP on pH and °Brix was not practically considerable. Total acidity was changed insignificantly by both treatments. Storage time caused a significant reduction in the pH and °Brix values of all samples. Total acidity of thermal and HHP of the treated samples remained stable until the 14^th and 29^th days, respectively. But then, there was a significant increase until the end of storage. The best retention of total phenolic (82.51%) and ascorbic acid (91.61%) content was observed by high-pressure processing during storage. Pectin methyl esterase was significantly inactivated by thermal and HHP processing, immediately after treatments. The residual activities were 5.69% and 3.59% at the end of the storage period, respectively. Initial contamination of control lime juice with acidophilic bacteria and total moulds and yeasts was 5.7 ± 0.51 and 4.79 ± 0.28 log CFU mL^-1, respectively. Immediately after treatment, high-pressure processing decreased the microbial load beyond detectable levels. The population of microorganisms remained stable until the 14^th day (for acidophilic) and during the whole time of the storage period (for total moulds and yeasts) under HHP processing.

Abstract Cinnamon is a highly valued aromatic spice widely used in the food and pharmaceutical industries. Due to its extensive applications, cinnamon is susceptible to food fraud, necessitating effective authentication methods. This study uses FTIR spectroscopy and spectral pattern analysis to establish a reliable method for detecting fraud in cinnamon powder by comparing authentic and adulterated samples. The ultimate goal is to provide a practical methodology for identifying adulteration in the spice industry. In this work, Fourier-transform infrared spectroscopy (FTIR) was employed alongside pattern recognition techniques for the authentication of cinnamon powder. To detect and classify fraudulent samples (adulterants include soybean powder, hazelnut shell powder, and dry bread powder, in different concentrations of 5%, 10% and 15% (w/w)) nondestructively, Principal Component Analysis (PCA) was utilised as an unsupervised method, while Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modelling of Class Analogy (SIMCA) were applied as supervised pattern recognition approaches. The results indicated that while the spectral data of the samples were effectively classified using the PCA technique, some overlap between specific groups was noted. The overall classification accuracy achieved by the SIMCA and PLS-DA classifiers was 83% and 90%, respectively. Based on PCA modelling, SIMCA enabled the classification of samples into two distinct groups (pure vs. adulterated), and the classification accuracy of this two-class model reached 100%. These findings confirm the effectiveness of combining FTIR with pattern recognition techniques for robust and nondestructive authentication of adulterated cinnamon powder, contributing to quality control in the spice industry.

Abstract Introduction: Tractors are considered as the main power generators in mechanized agriculture. Hence, the experts and engineers in tractor manufacturing of the country, are required to focus on developing and designing new features in tractor manufacturing. This must be, of course, paralleled with the economic aspects. Achieving this goal, Iran Tractor Manufactories Co., (ITMCO) has designed and developed tractors equipped with turbochargers. This has been performed on ITM800 & ITM485 models, according to world standards. The turbocharger system, with harnessing of lost energy in engine output fumes, compresses the air entering the engine and more air enters the cylinder. This will cause the engine to burn fuel more efficiently and thus produce more power.
Materials and Methods: This study has been carried out on ITM485 & ITM800 tractors (with turbocharger system) and ITM285 & ITM475 tractors (without turbocharger system) to assure the improvement of engine performance and compare them employing OECD world standards. Experiments were performed in the concrete runway of Tabriz Tractor Manufacturing Company. For experiments, a dynamometer was used to measure the traction force between two tractors, a measuring unit for fuel, a thermometer unit and a timer to measure the quantities of fuel consumption, drawbar force and power. For drawbar traction test, each of the tested tractors pulled the rear tractor in different gears and the dynamometer between these 2 tractors recorded the tractors traction force by data loggers. To measure tractors fuel consumption, a measuring unit of fuel (VDO - EDM 1404) was used that calculated the flow rate in the path of fuel from the fuel tank to the engine and the return path from the engine to the fuel tank and showed the quantity of fuel consumption in liters per hour digitally.
Results and Discussion: In comparison of traction power and force of tractors with turbochargers and without turbochargers in different gears, the results of variance analysis showed that the effect of tractor was significant. Traction power and force at tractors with turbochargers ITM485 and ITM800 and without turbocharger ITM475 have a significant difference in the level of one percent. Tukey post hoc test results also indicate that traction power and force in tractors with turbochargers ITM485 and ITM800 are significantly more than the tractor without turbocharger ITM475. The gear effect is also significant. Traction power and force in different gears have significant difference at the probability of one percent. Tukey post hoc test results indicate that power quantity is highest in the gears: (1+H, 2*H, 1*H, 3+L) and minimum in the gears: (1*L, 1+L, 2*L), (* Turtle and + Rabbit). But Tukey post hoc test results indicate that traction force quantity is highest in the gears: (1*L, 2*L, 1+L) and minimum in the gears: (2*H, 1+H). In the comparison of specific fuel consumption of tractors with turbochargers and without turbochargers in different gears, the results of variance analysis showed that the effect of tractor was significant. The amount of specific fuel consumption at tractors with turbochargers ITM485 and ITM800 and without turbocharger ITM475 has a significant difference in the level of one percent. Tukey post hoc test results also indicate that specific fuel consumption quantity in tractors with turbochargers ITM485 and ITM800 in the level of one percent is significantly less than the tractor without turbocharger ITM475. The gear effect is also significant. The specific fuel consumption quantity in different gears has significant difference at the probability of one percent. Tukey post hoc test results indicate that specific fuel consumption quantity is highest in the gears: (1*L, 1+L, 2*L) and minimum in the gears: (1+H, 2*H, 1*H).
Conclusions: The tests were performed on tractor drawbar traction. Results of variance analysis in this experiment on a concrete surface, indicated that the calculated traction power and force of ITM485 and ITM800 tractors (with turbocharger system) were higher than the ITM475 & ITM285 tractors (without turbocharger) and this difference was significant at the one percent level of probability. Meanwhile specific fuel consumption in the ITM485 and ITM800 tractors (with turbocharger system) was lower than that of the ITM475 & ITM285 tractors (without turbocharger) and this difference was significant at the one percent level of probability. This will lead to significant savings in fuel consumption.

Abstract Due to the importance of weed control and the limitations of mechanical methods in some places, in this research the water jet cutting for weed control was investigated. The cutting tests were performed on camel thorn weed in Shahid Bahonar university of Kerman. The water jet pressure of 90 bars was achieved with the aid of a suitable pump. The cutting time was studied in a completely randomized factorial design experiment (CRD) with five replications. Factors of experiments are: stem diameter in 2 levels (smaller and larger than 5 mm), distance of spraying jet from weeds in 3 levels (10, 20 and 30 cm) and two types of plant holders: blade and plate. The results showed that stem diameter and jet distance from the weed stem had significant effects on cutting time (at the 1%). The mean comparison of parameters showed that with increase of stem diameter the cutting time increased and any increase in jet distance from the weeds decreased the cutting time linearly with R2=0.96 and R2=0.99 for small and large diameter weeds, respectively. The minimum cutting time was measured at 30 cm of the jet from small diameter of stems. A multivariate linear regression model was also proposed for cutting weed parameters. It can be concluded that due to the flexibility of water jet cutting for restricted places, hydrodynamic control of weeds is proposed as a complementary method and sometimes a competing substitute method.

Abstract The main method of rice planting in Iran is transplanting. Due to poor mechanization of rice production, this method is laborious and costly. The other method is direct seeding in wet lands which is performed in the one third of rice cultivation area of the world. The most important problem in this method is high labor requirement of weed control. In order to compare the different rice planting methods (direct drilling, transplanting, and seed broadcasting) a manually operated rice direct seeder (drum seeder) was designed and fabricated. The research was conducted using a randomized complete block design with three treatments and three replications. Required draft force, field efficiency, effective field capacity, yield, and yield components were measured and the treatments were compared economically. Results showed that there were significant differences among the treatments from the view point of rice yield at the confidence level of 95% i.e. the transplanting method had the maximum yield. A higher rice yield was obtained from the direct seeder compared to the manual broadcasting method but, the difference between these two methods for crop yield was not significant even at the confidence level of the 95%. The coefficient of variation of seed distribution with direct seeding was more than 20%. The labor and time requirements per hectare reduced to 7 and 20 times, respectively when comparing the newly designed direct seeder with the transplanting method. The direct seeding method had the highest benefit to cost ratio in spite of its lower yield. Therefore, this method could be recommended in the rice growing regions.

Abstract Introduction
Today, maximizing the efficiency of fuels and increasing the output power of diesel engines is considered inevitable due to the increasing need for energy resources, the reduction of fossil fuel resources, the need to maintain the environment, reduce air pollution, and limit the electricity supply and fuel supply. In the large cities of Iran, the problem of vehicle pollution is one of the main problems. The lack of proper fuel, soot filters, and absence of requirement for a technical inspection of diesel vehicles have led to an increase in mortality and the growth of lung cancer due to pollution. All of studies indicate that fossil fuels, despite the low cost of production, will increase the cost of both living and environment. A solution for this crisis is to reduce the sources of pollutant-producing sources from the source of these pollutants. In the internal combustion engines, the compression ratio and alternative fuels are two important factors affecting engine performance and exhaust emission.
Materials and Methods
In this research, a one-dimensional computational fluid dynamics solution with GT-Power software was used to simulate a six-cylinder diesel engine to study the performance and exhaust emissions with different compression ratios and alternative fuels. The compression ratio was chosen to be 15:1 to 19:1 with an interval at unity. Alternative fuels such as (as base diesel), methanol, ethanol, diesel and ethanol, biodiesel and decane were selected. To modeling engine, first, all parts of the engine were introduced as a real six-cylinder engine, and then the required data were entered according to the actual engine conditions at the atmospheric pressure of one atmosphere. Before this investigation was carried out, a validation model for evaluation was done by experimental and simulation data. The validation results showed that software model error is acceptable and the model has a good capability of fitting and predicting.
Results and Discussion
The engine performance was evaluated in terms of engine power, engine torque, and specific fuel consumption at different engine compression ratio and fuel. The results showed that with increasing the compression ratio, brake power and brake torque increased. Among the fuels used in this engine, the maximum brake power and brake torque in the compression ratio of 19 with the decane fuel were 3.86% higher than that the base fuel and the lowest value was awarded in the compression ratio of 15, with methanol fuel and it was equal with 56.04%. The results indicated that by increasing compression ratio, the brake specific fuel consumption was reduced due to more power than the fuel consumed in the engine. A fuel with lower heating value should be injected more mass to the engine. This will increase the brake specific fuel consumption. In this research, the decane fuel with a compression ratio of 19 with a reduction of 3.72% had the lowest brake specific fuel consumption among other fuels. The CO emission from the engine largely depended on the fuel's properties, the availability of oxygen, the fuel mix with air, temperature, and turbulence inside the combustion chamber. The results highlighted that by increasing compression ratio, CO emission increased and CO emission in biodiesel fuel, with a compression ratio of 15, was decreased by 82.37% compared to the base. CO₂ emissions are not too harmful to humans, but they increase the potential for ozone depletion and global warming. With increasing compression ratio, CO₂ and HC emissions increased for all fuels, CO₂ emissions have risen up the base. The fuel heating mechanism, combustion temperature, oxygen content, and gas fuel availability are the most important factors in the formation of NOx. With increasing the compression ratio, the amount of NOX increases, which is due to the high temperature in the cylinder at a higher compression ratio. The viscosity and density of fuels have an effect on NOX emission, and because of the larger droplets of the fuel, it released NOX. The highest NOx emissions from biodiesel fuel are due to the high oxygen content of this fuel and the lowest NOx emissions from decane fuel, due to the low density of the fuel compared to other fuels.
 Conclusion
The results of this study showed that the decane fuel with a compression ratio of 19 in total had the best functional and pollutant characteristics among the six fuel used in this study. Therefore, this fuel can be the best alternative for diesel fuel.

Abstract Introduction
Various methods have been performed to control weeds in the world and the use of herbicides is one of them, but public concerns about human health have changed interest in alternative methods. Thermal methods based on flame-weeder, hot air, steam, and hot water have the potential to control weeds, but due to the high cost are not economical. Electromagnetic waves transfer energy into weeds and finally destroy them. The effect of radiation on plant mutation, high consumption of energy, and human health are problems for this approach. Unlike other methods, electrical energy is an ideal and non-chemical method for weeds. This method applies high voltage to weeds, their roots, and soil so that electric currents pass through them, and the vaporization of the liquid content of weeds kills the weeds. To increase the severity of damage to weeds, the development of a feedback mechanism is required. The ultrasonic sensor measuring physical parameters like plant height is a simple method. Some complex sensing systems include optical sensors such as infrared, and machine vision that require high-speed processors and expensive equipment. In this project, as a simple method, the monitoring of the electrical current passing through weeds was used for developing the feedback mechanism and increasing electric damage to weeds.
Materials and Methods
In this study, the system consisted of a high-voltage device that generated a 15 kV AC voltage to kill weeds, as well as a feedback mechanism that included a sensor to measure the electric current on the input of the weed killer and identify the presence of weeds and their annihilation. All parts were installed on a robotic platform, and an application on a laptop was connected to it via an access point for navigation and data reception. The system was tested in a greenhouse lab with various weeds. Initially, a test was performed to investigate the effect of high voltage on the weeds and establish relationships between the electric currents passing through weeds and their presence (before and after annihilation). During the test, the system was guided along a path and applied high voltage to kill the weeds. The feedback mechanism was then calibrated based on the extracted data on electric current relations. This allowed the system to detect weeds and their annihilation, enabling it to move to the next target once a weed had been eliminated. After calibration, a comparative test was conducted to evaluate the weed-killing efficiency of the two methods (with and without the feedback mechanism), and the results were analyzed using a t-test with p ≤ 0.01.
Results and Discussion
The observations indicated that the input electric current on the weed killer was dependent on the electric current passing through weeds. When the high-voltage electrode touched a weed, the electric current passed through it increased, and simultaneously, the high electrical energy destroyed the weed. After the removal of the weed, the electric current rapidly decreased. The average energy consumption per weed plant was estimated to be 250 joules, which can be compared with other methods. The final test comparing the use and non-use of the feedback mechanism revealed significant differences (P < 0.01) between the results obtained with and without the mechanism, demonstrating that the feedback mechanism increased the efficiency of weed annihilation. The sensing system used in the developed feedback mechanism is a simple method that is affected by the electrical resistivity of weeds. As such, it did not mistakenly detect other objects as weeds, unlike an ultrasonic mechanism. Based on these results, monitoring the electrical current passing through weeds proved to be a suitable method for developing a feedback mechanism for the weed killer to identify the presence of weeds and their annihilation.
Conclusion
The use of high voltage as a non-chemical and alternative method for weed control has shown promising results. The study revealed that measuring the electric current applied to the weed killer was an effective and straightforward approach to developing a feedback mechanism. This mechanism aids in identifying the presence of weeds and ensuring their elimination by intensifying the damage inflicted on them through the application of high electrical energy. To further enhance the efficiency and speed of weed control, future research should consider integrating an automatic guidance mechanism with the weed killer.

Abstract Within the last few years, a new tendency has been created towards robotic harvesting of oranges and some of citrus fruits. The first step in robotic harvesting is accurate recognition and positioning of fruits. Detection through image processing by color cameras and computer is currently the most common method. Obviously, a harvesting robot faces with natural conditions and, therefore, detection must be done in various light conditions and environments. In this study, it was attempted to provide a suitable algorithm for recognizing the orange fruits on tree. In order to evaluate the proposed algorithm, 500 images were taken in different conditions of canopy, lighting and the distance to the tree. The algorithm included sub-routines for optimization, segmentation, size filtering, separation of fruits based on lighting density method and coordinates determination. In this study, MLP neural network (with 3 hidden layers) was used for segmentation that was found to be successful with an accuracy of 88.2% in correct detection. As there exist a high percentage of the clustered oranges in images, any algorithm aiming to detect oranges on the trees successfully should offer a solution to separate these oranges first. A new method based on the light and shade density method was applied and evaluated in this research. Finally, the accuracies for differentiation and recognition were obtained to be 89.5% and 88.2%, respectively.

Abstract This study deals with the application of the Microsoft Excel for the estimation of the power requirements of some tillage implements. The mathematical formulas embedded in the spreadsheet file have been developed in the previously published papers; however, those formulas were augmented herein in order to contain some agricultural mechanization issues. Another feature of this article is the ability of the spreadsheet to generate trend curves automatically. The comparison of the power expenditure aspects of different tillage implements as well as the inspection of the effect of an arbitrary selected input parameter on the spreadsheet outputs were effectively performed. Numerically, the specific work of the rotary tiller was estimated two times to five times higher than the specific work of drawing implements. Furthermore, as an example of trend curves derived in this article, the increase in disc angle in the range of 25° to 70° reduced the draft and power needs of the disc plow by 66% and 54%, respectively. However, it increased the disc plow specific draft and power by 34% and21%, respectively.