Research Article-en
Precision Farming
F. Fatehi; H. Bagherpour; J. Amiri Parian
Abstract
Manually picking the flowers of the Damask rose is significantly challenging due to the numerous thorns on its stems. Consequently, the accurate detection of bloomed Damask roses in open fields is crucial for designing a robot capable of automating the harvesting process. Considering the high speed and ...
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Manually picking the flowers of the Damask rose is significantly challenging due to the numerous thorns on its stems. Consequently, the accurate detection of bloomed Damask roses in open fields is crucial for designing a robot capable of automating the harvesting process. Considering the high speed and precise capabilities of deep convolutional neural networks (DCNN), the objective of this study is to investigate the effectiveness of the optimized YOLOv8s model in detecting bloomed Damask roses. To assess the impact of the YOLO model size on network performance, the precision and detection speed of other YOLO network versions, including v5s and v6s, were also examined. Images of Damask roses were taken under two lighting conditions: normal light conditions (from civil twilight to sunrise) and intense light conditions (from sunrise to 10 AM). The outcomes demonstrated that YOLOv8s exhibited the highest performance, with a mean average precision (mAP50) of 98% and a detection speed of 243.9 fps. This outperformed the mAP50 and detection speed of YOLOv5s and YOLOv6s networks by margins of 0.3%, 6.1%, 169.3 fps and 198.6 fps, respectively. Experimental results show that YOLOv8s performs better on images taken in normal lighting than on those taken in intense lighting. A decline of 5.2% in mAP50 and 2.4% in detection speed signifies the adverse influence of intense ambient light on the model's effectiveness. This research indicates that the real-time detector YOLOv8s provides a feasible solution for the identification of Damask rose and provides guidance for the detection of other similar plants.
Research Article-en
The relationship between machine and soil
T. A. Medhn; A. G. Levshin; S. G. Teklay
Abstract
The efficient use of agricultural machinery significantly improves both the quantity and quality of field operations; therefore, it is essential to optimize operational speed and field time. Factors such as field shape complexities and soil surface roughness (SSR) significantly impact seeding performance. ...
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The efficient use of agricultural machinery significantly improves both the quantity and quality of field operations; therefore, it is essential to optimize operational speed and field time. Factors such as field shape complexities and soil surface roughness (SSR) significantly impact seeding performance. The objective of this research was thus to evaluate how these key factors affect seeder performance: (1) field size and shape, and (2) the interaction of seeder speed and SSR. The performance metrics, effective field capacity (Feff), efficiency (η), and average working speed (va), were analyzed using SAS software. The convexity (Icon) and rectangularity (IR) indices for each plot were calculated using the ArcGIS minimal bounding geometry Data Management tool, while the elevation standard deviation (σe) was computed using Python. The resulting values for Feff, η, and va varied widely, with values ranging from 10.2 to 3.1 ha h-1, 30% to 65.7%, and 5.2 to 17 km h-1, respectively. A va process capability index (Cpk) of 0.22 indicates a significant challenge in meeting the established limits. As the plot run-length increased, the Feff also increased (R2 = 42%), while it decreased with a rising perimeter to area ratio (P/A) (R2 = 51%). Additionally, Feff exhibited an upward trend as the Icon and IR indices rose, while it experienced a decline with greater compactness (Icom) and square perimeter (Isp) indices; albeit these relationships were not statistically significant. Higher roughness levels generally resulted in a decline in η. Furthermore, operating the planter at higher speed on uneven terrain led to a significant decrease in efficiency. Hence, redesigning the plots to minimize border complexities, eliminating topographic abnormalities, and implementing tailored plot-specific pre-sowing procedures, will significantly enhance planter performance.
Research Article-en
Modeling
M. Rafiei; F. Khoshnam; M. Namjoo
Abstract
In the current study, the modeling and optimization of various seedling growth and germination indices for parsley seeds were investigated. A lab-scale quadrupole magnetic field was developed, and experiments were conducted using a completely randomized factorial design with three replications. The factors ...
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In the current study, the modeling and optimization of various seedling growth and germination indices for parsley seeds were investigated. A lab-scale quadrupole magnetic field was developed, and experiments were conducted using a completely randomized factorial design with three replications. The factors considered were magnetic field intensity (150, 300, and 450 mT), exposure time (30, 60, and 90 minutes), and culture time (0, 7, and 14 days after applying the magnetic field). The results revealed that the magnetic field significantly affected shoot length, fresh root weight, and fresh shoot weight, while exposure time significantly impacted root length. Sowing day also significantly influenced root length and fresh root weight, along with other factors. Immediate sowings after magnetic field application enhanced root length, while sowing 14 days following the exposure increased shoot length, fresh root weight, and fresh shoot weight. A 30-minute exposure to magnetic field intensities of 150 to 300 mT did not significantly affect seedling growth parameters. However, higher field strengths of 450 mT for 60 to 90 minutes proved beneficial, leading to enhanced shoot length, fresh root weight, fresh shoot weight, germination rate, germination percentage, and reduced mean germination time. The analysis and optimization using Response Surface Methodology revealed that the optimal magnetization condition, with a desirability of 0.682, was achieved at a magnetic field of 450 mT, an exposure time of 60 minutes, and sown 14 days post-exposure. Higher magnetic fields appeared to enhance field durability and significantly impact seedling growth indices.
Research Article-en
Image Processing
I. Ahmadi
Abstract
In the context of plant diseases, the selection of appropriate preventive measures, such as correct pesticide application, is only possible when plant diseases have been diagnosed quickly and accurately. In this study, a transfer learning model based on the pre-trained EfficientNet model was implemented ...
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In the context of plant diseases, the selection of appropriate preventive measures, such as correct pesticide application, is only possible when plant diseases have been diagnosed quickly and accurately. In this study, a transfer learning model based on the pre-trained EfficientNet model was implemented to detect and classify some diseases in tomato crops, using an augmented training dataset of 2340 images of tomato plants. The study's findings indicate that during the model's validation phase, the rate of image categorization was roughly 5 fps (frames per second), which makes sense for a deep learning model operating on a laptop computer equipped with a standard CPU. Furthermore, the model was learned well because increasing the number of epochs no longer improved its accuracy. After all, the curves of the train and test accuracies, as well as the losses versus epoch numbers, remained largely horizontal for epoch numbers greater than 20. Notably, the highest coefficient of variation across these four cases was only 7%. Furthermore, the cells of the primary diagonal of the confusion matrix were filled with larger numbers in comparison with the values of the other cells; precisely, 88.8%, 7.7%, and 3.3% of the remaining cells of the matrix (cells of the primary diagonal excluded) were filled with 0, 1, and 2, respectively. The model's performance metrics are: sensitivity 85%, specificity 98%, precision 86%, F1-score 84%, and accuracy 85%.
Research Article-en
Post-harvest technologies
C. N. Onwusiribe; J. Mbanasor; P. O. Nto; M. C. Ndukwu
Abstract
Rice is a major staple food consumed worldwide, but its processing has significant environmental impacts due to water and energy consumption and greenhouse gas emissions. As a result, rice producers are adopting sustainable processing techniques to reduce negative environmental impacts and increase profitability. ...
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Rice is a major staple food consumed worldwide, but its processing has significant environmental impacts due to water and energy consumption and greenhouse gas emissions. As a result, rice producers are adopting sustainable processing techniques to reduce negative environmental impacts and increase profitability. This study analyzed the sustainability of modern and traditional paddy rice processing techniques among smallholder rice farmers in Southeast Nigeria. The data was collected from 240 rice producers using statistical approaches such as descriptive statistics, sustainability indicator (Weight Assessment Ratio Analysis), and multinomial regression analysis. The results showed that 34.7% of rice farmers used modern processing techniques while 65.3% used traditional methods. Traditional milling produced substantial carbon emissions, according to 77% of small-scale farmers, while 68% rated noise pollution as high. 80-100% of small-scale farmers using modern techniques cared about the environment and wanted to reduce their gas emissions, solid waste, energy use, and water use. The sustainability index for farmers using traditional and modern processing techniques was affected by gender experience, labor size, investment, income, cost of production, understanding of climate change, and environmental sustainability. The study recommends using renewable energy sources to increase productivity and reduce environmental effects.
Research Article-en
The relationship between machine and soil
H. Asadollahi; B. Mohammadi-Alasti; A. Mardani; M. Abbasgholipour
Abstract
Understanding soil deformation dynamics is critical in various fields, such as off-road vehicle mobility, agriculture, and soil mechanics. In particular, evaluating soil-tire interactions is essential for optimizing energy consumption and minimizing the negative effects of soil compaction. This study ...
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Understanding soil deformation dynamics is critical in various fields, such as off-road vehicle mobility, agriculture, and soil mechanics. In particular, evaluating soil-tire interactions is essential for optimizing energy consumption and minimizing the negative effects of soil compaction. This study investigates the effect of soil deformation rates on the pressure-sinkage relationship and energy consumption using a controlled soil bin environment and a bevameter system. The primary objective of the study is to examine how different traffic levels and varying penetration rates influence the energy required to achieve specific sinkage depths. The study employed a completely randomized block design, with each treatment replicated three times to ensure precision and reliability. Quantitative measurements were obtained using a load cell attached to a bevameter, capturing the forces at a sampling frequency of 30 Hz. Results demonstrated a significant influence of both traffic level and penetration velocity on soil resistance and energy consumption. For the larger plate, the pressure required for penetration increased with higher velocities and traffic levels. At the highest velocity (45 mm s-1) and with 8 passes, the pressure needed for sinkage was maximal. The energy consumption for each scenario was calculated by integrating the area under the force-sinkage curve. The analysis of variance (ANOVA) revealed that the number of wheel passes, plate size, and penetration velocity significantly affected energy consumption. At the highest sinkage depth (60 mm), the energy consumption for the larger plate at 45 mm s-1 and with 8 passes was nearly double that of the smaller plate. These results emphasize the importance of considering both traffic-induced compaction and velocity when designing off-road vehicles or agricultural machinery that interact with deformable terrains.
Research Article-en
Image Processing
O. Doosti Irani; M. H. Aghkhani; M. R. Golzarian
Abstract
Robotic harvesting in agriculture is an effective method for producing healthy fruit, reducing costs, and increasing productivity. Detecting and harvesting sweet peppers, however, remains a challenging task. This study aims to develop an unsupervised machine vision algorithm to recognize colored sweet ...
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Robotic harvesting in agriculture is an effective method for producing healthy fruit, reducing costs, and increasing productivity. Detecting and harvesting sweet peppers, however, remains a challenging task. This study aims to develop an unsupervised machine vision algorithm to recognize colored sweet peppers using a combination of geometric features (Fast Point Feature Histogram- FPFH) and color features (H, S, and V). Depth images were captured using a Kinect v2 sensor, and a 3D model was reconstructed. After extracting the geometric and color features, data preprocessing involved undersampling to ensure balance and applying the Z-score criterion to eliminate outliers. Principal component analysis (PCA) was used to reduce the feature dimensions, and the K-means clustering model was implemented to categorize the data using six geometric features and three color features. The silhouette coefficient was employed to evaluate clustering quality, and human evaluation demonsterated that the algorithm achieved a detection accuracy of 95.10% for sweet peppers.
Research Article-en
Agricultural systems engineering (greenhouse, fish farming, mushroom production)
M. M. Naserian; R. Khodabakhshian
Abstract
The buildings and the agri-food sectors nearly consume 40% and 21% of the world's total energy, respectively. This research aims to combine these two significant energy-consuming sectors to decrease the total society’s energy consumption. For this purpose, a novel small-scale building integrated ...
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The buildings and the agri-food sectors nearly consume 40% and 21% of the world's total energy, respectively. This research aims to combine these two significant energy-consuming sectors to decrease the total society’s energy consumption. For this purpose, a novel small-scale building integrated agriculture system was designed and constructed. In this research, the total energy and water consumption, annual CO2 production, and the total cost of employing the novel system were analyzed from the building residents’ and social points of view. Moreover, the results were compared with the total results of a building and a separate standard greenhouse with the same product. The results show that the total energy reduction because of using the novel system was 31.2%. According to the results, the novel system will cause approximately 3400 kgCO2 emission reduction over a life cycle of 20 years. Moreover, yearly water consumption reduction was 19.2 L kg-1 of lettuce production. The payback period was approximately 5 years based on the cost analysis results comprising investment, operational, and social costs. Sensitivity and Scenarios analyses were conducted to better understand the effect of probable influential parameters and make the investment for the novel system secure and attractive.
Review Article-en
The relationship between machine and soil
S. Manoj Kumar; R. Karthikeyan; K. Thirukumaran; A. Senthil; P. Dhananchezhiyan
Abstract
The traditional method of transplanting rice seedlings is labor-intensive, prompting a shift towards direct seeding of rice as an alternative crop establishment method. Direct seeding offers several advantages, including reduced labor requirements, timely sowing, and water conservation. Innovations in ...
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The traditional method of transplanting rice seedlings is labor-intensive, prompting a shift towards direct seeding of rice as an alternative crop establishment method. Direct seeding offers several advantages, including reduced labor requirements, timely sowing, and water conservation. Innovations in machinery have significantly enhanced the efficiency of direct-seeded rice cultivation, spanning advancements from land preparation to harvest. Techniques such as no-till methods and laser leveling promote efficient resource utilization and water conservation while minimizing soil disturbance. Specialized seeders and precision seed meters ensure accurate seed placement and uniform germination. Power-operated seeders and hand-held rotary dibblers further improve sowing efficiency. Modern irrigation systems, including drip irrigation, alternate wetting and drying, and automated soil moisture sensing, optimize water productivity. Weed management has advanced with mechanical, solar-powered, and autonomous weeding technologies. Additionally, crop mapping, variable rate technology, and unmanned aerial vehicles enable precise and site-specific weed control. Overall, modern machinery has transformed direct-seeded rice cultivation, resulting in increased input use efficiency, reduced labor demands, higher crop yields, and improved sustainability. Continued innovation offers significant potential for optimizing plant establishment, minimizing post-harvest losses, enhancing profitability, and conserving natural resources. This review article examines these advancements and their implications for the future of direct-seeded rice cultivation.
Review Article-en
Agricultural systems engineering (greenhouse, fish farming, mushroom production)
M. Bamdad; M. Zangeneh; S. H. Peyman
Abstract
Agricultural cooperatives (ACs) play a vital role in the global agricultural sector, yet their success in food production and supply varies significantly across countries. This study presents a comprehensive review of existing literature on ACs using the PRISMA methodology and proposes a methodological ...
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Agricultural cooperatives (ACs) play a vital role in the global agricultural sector, yet their success in food production and supply varies significantly across countries. This study presents a comprehensive review of existing literature on ACs using the PRISMA methodology and proposes a methodological framework to guide future research. Each selected study was analyzed based on four key dimensions: purpose, methodology, factors examined, and key findings. These variables were then categorized to enable a more robust comparative analysis. The review highlights that the success of ACs is driven by effective management, strong marketing strategies, and a dedicated workforce. Education emerges as a critical factor, irrespective of age or gender. However, strategies for success differ among cooperatives, underscoring the need for context-specific research to accurately assess the status and needs of ACs in various regions.
Review Article-en
Precision Farming
S. Rishikesavan; P. Kannan; S. Pazhanivelan; R. Kumaraperumal; N. Sritharan; D. Muthumanickam; M. Mohamed Roshan Abu Firnass; B. Venkatesh
Abstract
Drones have emerged as a promising technology in precision agriculture, supporting Sustainable Development Goals (SDGs) by enhancing sustainable farming practices, improving food security, and reducing environmental impact. This review article is intended to meticulously analyze the multiple applications ...
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Drones have emerged as a promising technology in precision agriculture, supporting Sustainable Development Goals (SDGs) by enhancing sustainable farming practices, improving food security, and reducing environmental impact. This review article is intended to meticulously analyze the multiple applications of drone technology in agriculture, such as crop health monitoring, pesticide and fertilizer spraying, weed control, and data-driven decision-making for farm optimization. It emphasizes the role of drones in precision spraying, promoting targeted interventions, and minimizing environmental impact compared to conventional methods. Drones play a vital role in weed management and crop health assessment. The paper focuses on the importance of data collected by drones to acquire the necessary information for decision-making concerning irrigation, fertilization, and overall farm management. However, using Unmanned Aerial Vehicles (UAVs) in agriculture faces challenges caused by batteries and their life, flight time, and connectivity issues, particularly in remote areas. There are legal challenges whereby regulatory frameworks and restrictions are present in different regions that affect the operation of drones. With the help of continuous research and development initiatives, the challenges depicted above could be solved, and the fullest potential of drones can be tapped for achieving Sustainable Agriculture.
