Journal of Agricultural Machinery

Abstract The present study investigated the use of the cyclic voltammetric electrochemical method and the electronic tongue (e-tongue) method for detecting adulteration in lime juice. Since the measurement of citric acid content in lime juice is an accepted indicator of lime juice adulteration in laboratories, at first, attempts were made to determine its concentration using a potentiostat device and the cyclic voltammetry method, which involved various electrodes including glassy carbon, graphite, gold, and carbon nanotube and gold nanoparticle-modified glassy carbon electrodes. Different conditions were considered by testing citric acid at multiple concentrations in buffers with different pH levels. The results showed that the electrochemical behavior of citric acid was weak, so conventional electrochemical methods could not be used to check its behavior. In the second part, a portable electronic tongue system (e-tongue) was evaluated. Eight samples of adulteration levels (from 5% up to 95%) were created in lemon juice (0, 5, 10, 20, 40, 70, 95, and 100% impurity). Unsupervised models including Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), and supervised models including Multilayer Perceptron (MLP) neural networks and Support Vector Machine (SVM) were used. Based on the results, the PCA fingerprint showed good discrimination between different levels of adulteration, and HCA further confirmed this. The results of the analysis of supervised methods showed that the MLP model outperformed the SVM model in predicting fraud levels with a success rate of 99.33% and high correlation coefficients (R² = 0.9973, RMSE = 0.09). These results show that the proposed system can separate different levels of adulteration in lemon juice and can be used as a taste quality control system.

Abstract Correct and timely diagnosis of plant diseases is crucial for improving crop performance. Therefore, developing a precise and reliable intelligent system for managing leaf diseases in trees is very important for farmers. This study aims to develop an artificial intelligence-based solution for detecting leaf diseases in quince trees using a state-of-the-art single-stage object detection model, YOLO (You Only Look Once). Images of diverse leaf diseases affecting this tree were collected from multiple sources, including agricultural research centers in Isfahan Province, Iran, relevant websites, and researchers. In this study, a transfer learning approach was employed to evaluate three well-known YOLO models (YOLOv5m, YOLOv7, and YOLOv8m) based on their detection and identification performance. Statistical metrics, including precision, recall, F1-score, and accuracy, were used to evaluate and compare the performance of the investigated models. The results indicate that the accuracy of the YOLOv5m, YOLOv7, and YOLOv8m models were 78%, 83%, and 87%, respectively. Experimental results revealed that YOLOv8m, trained from scratch on the dataset, demonstrates substantial capability in identifying leaf diseases in quince trees. In addition, a comparison showed that this model outperformed other investigated models with scores of 0.87, 0.66, 0.69, and 0.67 for accuracy, precision, recall, and F1-score, respectively. Based on the overall results of this research, the YOLOv8m model trained in this study can be introduced as a specialized tool for this particular crop. Therefore, the developed model in this study, specifically tailored to quince leaf diseases, can be integrated into diagnostic software for tree leaf diseases. Such software can assist farmers in accurately diagnosing diseases, ultimately reducing economic losses.

Abstract Seaweeds are well known for their technological, nutritional, and health values, and their preservation by drying is essential to stabilize and maintain the quality of the product during storage. The research presents the obtaining of mathematical models in polynomial functions using the response surface methodology. The influence of the independent drying variables was studied: load density (1.70-15 kg m^-2), incandescent lamp wattage (0-500 W), temperature (30-70 °C) and air velocity (0.5-2.5 m s^-1) on the response variables: global acceptance (--), total phenolic content (mg GAC/100 g_db) and drying time (min). The study also showed that the conditions of temperature and incandescent lamp wattage during drying significantly affected the total phenolic content. The optimum conditions were: load density 9.13 kg m^-2, incandescent lamp wattage 374.5 W, temperature and drying air velocity of 63.3 °C and 1.88 m s^-1, respectively. The results show that increasing the power of the incandescent lamps leads to a shorter drying time of approximately 40-45%. For these optimized conditions, mathematical models were applied to simulate the drying curve and kinetics of the material studied. Using the Quasi-Newton Simplex method, the models of Midilli et al. and Page in second place, achieved a better performance in the quality of fit of the curves to the experimental data. Under these conditions, the value of the effective diffusivity of water was of the order of 2.03×10^-11 m² s^-1, a value very similar to those published for agro-industrial products. The information obtained can be of great help in the use of the obtained parameters and applied techniques for the development of equipment and process control in the drying of red seaweed.

Abstract The Soil and Plant Analysis Development (SPAD) value is a significant parameter indicating chlorophyll content, particularly in the green parts of plants. Conventional SPAD meters determine this value by measuring the transmission and absorption of red and infrared radiation at a single point (2×3 mm² sensor size). However, obtaining a comprehensive value for an entire leaf requires multiple measurements, increasing processing time. In this study, a non-destructive method for predicting SPAD values was developed using image processing techniques to determine dominant wavelength values from leaf photographs. A custom-designed photo box with controlled 6000 lux white LED lighting was used to capture images at a fixed distance of 15 cm. Images were processed using Color Picker (2024) software, where green components of the leaf were analyzed to extract dominant wavelength values. The results demonstrated that SPAD values could be accurately predicted using dominant wavelength data, with a 98.33% accuracy for the linear model (RMSE: 1.308) and 98.43% for the polynomial model (RMSE: 5.467). The findings indicate that a linear model provides a more precise correlation. This novel approach enhances the efficiency of SPAD measurement and offers a rapid, non-destructive alternative to conventional methods.

Abstract Efficient control of agricultural machinery is crucial in sugar plants for maintaining product quality, managing operational costs, and improving productivity. The Ethiopian sugar industry is vital to the country's economy; however, issues with machinery management can lead to higher maintenance costs and poor operational efficiency. This study aims to evaluate the agricultural machinery management system at the Arjo Diddessa sugar factory and optimize operational costs. Between 2016 and 2022, data were collected through surveys, interviews, and observations. To improve machinery running costs, a linear programming model was studied using Linear Interactive and Discrete Optimizer )LINDO( software. The findings revealed that 49% of non-operational machinery required minor repair, whereas 14% required disposal. The anticipated work rate exceeded the actual rate by 35.33%. Among the tasks, uprooting exhibited the smallest variance at 5.73%, while inter-row cultivation displayed the greatest discrepancy at 67.21%. Initial repair expenses were minimal but increased as the equipment aged. The optimization model achieved a maximum reduction of 10.60% in operational costs during 2021-22, highlighting the importance of accurate machinery work rate estimation and performance analysis for enhancing efficiency. The study identified critical inefficiencies in machinery management and emphasized the need for robust maintenance systems and strategic replacement plans for aging equipment. Optimizing operational efficiency is essential for improving productivity and reducing costs in sugar production processes.

Abstract Soil properties play a fundamental role in the success of agricultural operations through their impact on crop growth and quality, as they determine their ability to retain water and absorb nutrients, and affect soil aeration and the root system. The aim of this study is to predict bulk density and resistance to soil penetration under different moisture levels during tillage operations. It includes four moisture levels: 7, 14, 22, and 28%, and three types of plows: the moldboard plow, chisel plow, and disc plow. Moreover, soil samples were collected at two depths: 15 cm and 30 cm. The change in the physical properties of the studied soil is also measured during the growth periods of wheat crop (after tillage, beginning of the season and end of the season). The study is conducted in Al-Qurna district, north of Basra Governorate, Iraq, in clay loam soil. The results are analyzed and mathematical equations are obtained to predict the studied properties using the response surface methodology. The obtained results indicate that soil moisture during plowing, plow type, soil depth, and crop growth periods have a significant effect on soil bulk density and penetration resistance. The 14% moisture treatment is superior, recording the lowest bulk density and lowest penetration resistance of 1.12 Mg m^-3 and 1133 kN m^-2, respectively. While the 28% moisture treatment provided the highest bulk density and highest penetration resistance of 1.22 Mg m^-3 and 1379 kN m^-2, respectively. The results also show that increasing the soil depth from 15 to 30 cm increases the bulk density and soil penetration resistance, by 12 and 45.70%, respectively. Plowing with a disc plow improves soil properties, giving the lowest bulk density and penetration resistance of 1.12 Mg m^-3 and 1074 kN m^-2, respectively. While using the chisel plow leads to recording the highest bulk density and penetration resistance, which reached 1.22 Mg m^-3 and 1442 kN m^-2, respectively. As for the moldboard plow, the bulk density and soil penetration resistance reached 1.18 Mg m^-3 and 1282 kN m^-2, respectively. The growth periods have a significant effect on the studied soil properties where the beginning of the growing season provided the lowest bulk density. The bulk density reached 1.17, 1.13, and 1.23 Mg m^-3 for the periods after plowing, at the beginning of the season and its end, respectively. While the penetration resistance after plowing is superior with the lowest resistance compared to the beginning of the season and its end, as it reached 897, 1327, and 1573 kN m^-2, respectively. The results of data analysis show that the obtained mathematical models accurately and efficiently predict bulk density and soil resistance to penetration under the experimental conditions, with a high coefficient of determination (R²) of 0.6460 and 0.8114 for the bulk density and penetration resistance, respectively.

Abstract The vibrations generated by the use of different fuel mixtures in tractor engines can lead to accelerated wear of engine components, significant increases in maintenance costs, and reduced comfort and safety for operators. Nowadays, renewable fuels, namely biodiesel and bioethanol, have been of great interest to many researchers. In the present study, vibrations of the engine of MF285 tractor were measured in three directions, at speeds of 1000, 1600, and 2000 rpm for ten different fuel levels obtained from different compositions of biodiesel, bioethanol, and diesel fuels. To analyze the effects of the concerned parameters on engine vibrations, the response surface methodology (RSM) and artificial neural network fuzzy inference system (ANFIS) were applied. The obtained results demonstrated that increasing the engine speeds was in direct proportion to the vibrations increase. Furthermore, pure diesel fuel accounted for the major portion of vibrations, and B₅E₄D₉₁ had the highest vibrations among the fuel compositions. Moreover, vibrations were meaningfully reduced with the increase of biodiesel in fuel compositions. The optimization analysis revealed that the most effective fuels, exhibiting the lowest vibration levels, were identified as B₂₅E₆D₆₉ through RSM and B₂₅E₄D₇₁ via ANFIS.

Abstract In this study, the drying process of apples was explored using a new combined solar dryer known as the Refractance Window-Parabolic Trough Collector (RW-PTC). The drying kinetics, energy efficiency in the solar collector and dryer, and the role of the dryer in reducing energy consumption and pollutant emissions during the drying process were investigated. Drying experiments were carried out with three energy sources, including conventional non-renewable energy (RW), solar-assisted drying (PRW), and fully solar drying (SRW). In the first and second methods (RW and PRW), drying was performed at three temperature levels (65, 75, and 85 °C), and in the third method (SRW), drying was performed at the temperature of the solar collector. The average optical and thermal efficiency of the PTC collector during the experimental hours were 62.01% and 49.31%, respectively. The lowest specific energy consumption was observed in the SRW method at 10.24 (kWh kg^-1). The results showed that the solar energy used in the combined drying methods of PRW-65, PRW-75, PRW-85, and SRW accounted for 54.91%, 52.62%, 48.85%, and 70.30% of the total energy consumption, respectively, and by the same amount, energy consumption from non-renewable sources was reduced. By using a solar collector in the PRW and SRW drying methods, the CO₂ emission was reduced by 54.64% and 80.94%, respectively, compared to the conventional RW method. Overall, the implementation of solar energy in the PRW and SRW methods improved energy parameters and reduced pollutant emissions during the drying process.

Abstract This study investigated the effects of walnut oil incorporation on the physicomechanical and structural properties of sodium carboxymethyl cellulose-based edible films, with a focus on two methods of oil addition: bilayer and composite configurations. For this purpose, firstly walnut oil Pickering emulsion (10% oil) was stabilized using chia seed gum, which was then incorporated into the formulation of bilayer and composite films. SEM revealed that bilayer film exhibited a more cohesive and homogeneous structure compared to the composite film. XRD analysis indicated a semi-crystalline amorphous structure across all films, with bilayer film displaying slightly sharper peaks than composite film. Moisture content and solubility tests highlighted the hydrophobic influence of walnut oil, with bilayer films exhibiting the lowest moisture content and solubility due to their surface-localized oil layer. Thermal analysis using DSC and TGA demonstrated improved thermal stability and reduced weight loss in bilayer film. Mechanical tests showed that the bilayer film had the highest elongation at break (34.3%) and the lowest tensile strength (3.4 MPa). Color analysis revealed significant changes in chromatic indices, with composite films showing higher saturation and total color difference. These findings underscore the potential of walnut oil emulsion stabilized with chia seed gum, particularly in bilayer configurations, to enhance the functional properties of sodium carboxymethyl cellulose-based films.

Abstract Polylactic acid (PLA) is a thermoplastic, biodegradable, and bioactive polymer obtained from renewable resources such as beets and potatoes. PLA is regarded as a polymer that is nearly brittle, which can restrict its applications in the packaging industry. The mechanical properties of this polymer can be improved by adding nanoparticles and plasticizers. In this research, zinc oxide nanoparticles (1 wt% of PLA), Polyethylene glycol 400 (20 wt% of PLA), and Polysorbate 80 (0.25 wt% of the solution) were used to improve the mechanical properties of PLA films. The effects of these materials on the films were measured at two time points: the first month and the tenth month, with the aim of investigating physical aging, a precursor to polymer degradation. Statistical analysis was performed on the mechanical properties measured during these periods to identify significant differences between the produced films. Results showed that the highest tensile strength (82.99± 1.90 MPa, neat PLA), elongation at break (76.82± 27.22 %, PLA/PEG/ZnO), toughness (20.13± 7.89 J cm^-3, PLA/PEG/ZnO), and Young's modulus (2.74± 0.10 GPa, neat PLA) were observed in the first month. Analysis of variance results regarding the effect of time on each film revealed that in most cases, the mechanical properties did not change significantly after ten months. Based on the stress-strain curves, it was found that the neat PLA film is among the resistant materials. The PLA/Polysorbate/ZnO film exhibited brittle behavior in the tenth month. The remaining samples exhibited characteristics that fell between resistant and ductile materials in both the first and tenth months.

Abstract In this study, X-ray computed tomography (CT) as a non-destructive method for internal quality evaluation of apple fruit was investigated. For this purpose, three local apple fruit cultivars including: Red Delicious, Golden Delicious, and Golab were used. The CT number of the images, which indicates the amount of X-ray absorption, was extracted using K-PACS software. Quality parameters such as the amount of soluble solids content, titratable acidity, flavor index, and pH of studied cultivars were measured. The relationship between quality parameters and CT number obtained from tomography images of fruits in the form of linear regression models was investigated. According to the results, the correlation between CT number and quality parameters in all models was more than 0.900. For different cultivars, CT number had a positive correlation with the amount of titratable acidity, flavor index, pH, and soluble solids. The evaluation of quality parameters for the Red Delicious cultivar had the highest accuracy, achieving coefficients of determination (R²) of 0.952 for flavor index, 0.964 for soluble solids, 0.941 for acidity, and 0.969 for pH. For all cultivars, the highest correlation was observed between the pH and the number of CT (with coefficients of explanation 0.969, 0.972, and 0.966 for Red Delicious, Golden Delicious, and Golab cultivars, respectively). This indicates that X-ray CT can reliably assess internal quality attributes without damaging the fruits. The established linear regression models provide a validated and reproducible method for non-destructive quality evaluation of apple fruits.

Abstract In this study, ultrasonic radiation (US), packaging film, controlled atmosphere packaging, and controlled storage temperature were utilized for tomaoto packaging. Prior to packaging, the samples underwent ultrasonic treatment and were subsequently packed using polyethylene film (PE) and polyethylene film equipped with 2% nanoclay particles (Nano film) under both normal atmospheric conditions and modified atmosphere (MA) (5% O₂ + 3% CO₂). These tomatoes were stored at 25°C and 4°C for 28 days. Weekly assessments of storage properties included an examination of physical aspects such as moisture and color indices, chemical factors like pH, total soluble solids (TSS), lycopene, and total phenolic content, as well as mechanical properties encompassing penetration force and elastic modulus. The results indicated that the storage had a detrimental effect on the trends of property changes. Utilizing a modified atmosphere, appropriate storage temperatures, and applying ultrasonic treatment and Nanofilm were found to regulate specific properties effectively. Statistical analysis revealed a significant impact of the applied treatments on most properties at both the 1% and 5% significance levels. On the other hand, an Artificial Neural Network (ANN) was employed for data prediction, and the results showed that the best structure in predicting the physical, mechanical, and chemical properties was 5-10-11. O₂ and CO₂ were predicted with high accuracy with R² = 0.93 and R² = 0.86, respectively, which has shown the accurate performance of the ANN in predicting the data with the selected structure.

Abstract This study evaluates the energy consumption and economic performance of three different weed control methods employed in olive orchards in Tarom County, Zanjan Province, Iran, with an emphasis on sustainable agriculture. The objective is to assess the energy efficiency and cost-effectiveness of different weed management systems. The analysis includes chemical weed control (System I), mechanical control (System II), and integrated weed management (System III). Data were collected through interviews with 50 olive farmers, supplemented by official agricultural records. Results show that total energy consumption was highest in System III (93,069.16 MJ ha^-1), and lowest in System I (64,297.16 MJ ha^-1). System I also demonstrated superior energy efficiency (0.74), output energy (47,648.40 MJ ha^-1), and energy productivity (0.06 kg MJ^-1), making it the most viable option for optimizing energy consumption. Economically, System I generated the highest net profit (4,662.28 $ ha^-1) and benefit-cost ratio (2.66), outperforming Systems II (3,073.31 $ ha^-1; BCR: 2.16) and III (2,953.57 $ ha^-1; BCR: 1.97). The study concludes that System I, with its efficient use of renewable energy, is the most viable option in terms of both energy and economic performance, providing a balance between low energy input and high yield, thus maximizing profits and minimizing production costs. These findings emphasize the importance of selecting appropriate weed control methods to optimize energy use and reduce overall production costs in olive cultivation.

Abstract Influence of a single atmospheric component or meteorological variable on the host, pathogen, or their interaction in controlled environments has accounted for the majority of climate change’s impact on plant pests and diseases. Climate change can lead to alterations in the stages and rates of growth of pests and diseases, host resistance, and the physiology of host-pathogen or host-pest interactions, which can cause substantial harm and reduce tomato crop yields. Different approaches have been ineffective in the accuracy of pest and disease forewarning in past years. The remarkable progress in Deep Convolutional Neural Networks (DCNNs) is revolutionizing the early detection of pests and diseases in crops. By analysing vast amounts of present and historical climate data, alongside their expertise in object identification and image categorization, these AI models can predict outbreaks with impressive accuracy. However, understanding the specific microclimate suitable for each pest and disease is crucial for truly effective intervention. Combining these two elements creates a powerful, targeted approach to preserving crops. A forewarning system can help to reduce the use of pesticides, thereby reducing the cost of production and environmental pollution. Proper cloud servers and IoT-based sensor networks should be used for a better forewarning of pests and diseases in future circumstances.

Abstract As a growing global concern, water and climate changes have had a notable influence on agriculture. The main factor of this issue is food security, particularly in terms of food production and food pricing. This study investigates how climate change affects food security in Vietnam and Ghana, where agriculture is essential to socio-economic growth. The main study methodologies include ethnographic techniques and in-depth interviews with 50 farmers in each nation; 100 farmers in total. Results show that agricultural production and farmer health in these areas are highly vulnerable to increasing temperatures and erratic precipitation patterns. Vietnamese farmers mainly face flooding, sea-level rise, and saltwater intrusion, which endangers rice production, whereas Ghanaian farmers are more susceptible to droughts, which limit the amount of water available for rain-fed agriculture. Food security necessitates a change to alternate, robust crop types and improvements in agricultural technologies to counter these risks. The study highlights the need for adaptive measures such as enhanced irrigation systems, drought-resistant seeds, and early-warning systems for severe weather. These insights can help governments, agricultural stakeholders, and consumers develop policies and practices that improve food quality and stability, supporting sustainable agriculture.