Research Article-en
The relationship between machine and soil
T. A. Medhn; A. G. Levshin; S. G. Teklay
Articles in Press, Corrected Proof, Available Online from 30 December 2024
©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).
https://doi.org/10.22067/jam.2024.89995.1286
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
Design and Construction
A. Sadin; M. H. Aghkhani; M. A. Ebrahimi-Nik; J. Baradaran Motie
Articles in Press, Accepted Manuscript, Available Online from 08 March 2025
©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)
https://doi.org/10.22067/jam.2023.84424.1190
Abstract
IntroductionPlanting rice seedlings in the main field followed by periodic or intermittent irrigation is often considered a form of dry farming. Research suggests that flood irrigation in rice cultivation is primarily favored by farmers for its ability to control weeds and ensure a reliable water supply, ...
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IntroductionPlanting rice seedlings in the main field followed by periodic or intermittent irrigation is often considered a form of dry farming. Research suggests that flood irrigation in rice cultivation is primarily favored by farmers for its ability to control weeds and ensure a reliable water supply, rather than necessarily enhancing rice quality or yield. Depending on the rice variety, intermittent irrigation can sometimes improve both the quality and yield per unit area. The transplanting process in this method can be carried out manually without machinery or through mechanized methods using a planter.Materials and MethodsConventional rice transplanters designed for use in flooded land are not suitable for transplanting in dry land farming due to technical constraints. Therefore, it is necessary to develop a specialized rice transplanter tailored for such soil condition. This transplanter encompasses essential components, including a furrow opener, coverer, seedling storage tank or tray, seedling mechanism (distributor), seedling transfer mechanism (seedling transport piston), end separator for seedlings in the soil, power transmission system, depth adjustment shoe, and main and sub chassis. To evaluate the planter's performance, various parameters were assessed, including the percentage of lost plants, the average vertical angle of plant orientation, the average spacing between plants in the crop row, and the average number of seedlings per plant. Moreover, a factorial randomized block design was employed, with three replications for each level of the independent variables. The independent variables were forward speed (X1) at three levels of 0.25, 0.5, and 1 m s-1, planting depth (X2) at three levels of 4, 8, and 12 cm, and the size of the outlet opening of the seedling tray (X3) in three levels of 10, 15, and 20 mm.Results and DiscussionThe developed single-row planter features key specifications, including a working width of 250 mm, a power requirement of 0.57 kW, a theoretical field capacity of 0.06 ha h-1, and a field efficiency of 66.67%. The research findings revealed that forward speed, planting depth, and outlet opening size, along with their interactions, significantly impact the percentage of lost plants at the 99% confidence level. Among the three levels of forward speed (X1), the best speed level is 0.25 m s-1, as it results in the lowest percentage of lost seedlings. As the forward speed increases, the percentage of lost seedlings increases. The lowest percentage of lost plants (Y1) occurs at the planting depth of 8 cm and an outlet opening size of 20 mm. Furthermore, forward speed, planting depth, and their interaction have a noteworthy influence on the vertical angle of plants are established, at the 99% confidence level. With the increase of forward speed and planting depth, the average vertical angle of seedling establishment deviates from the vertical position. The forward speed of 0.25 meters per second and the planting depth of 8 cm show the best results for the establishment of seedlings. The sole factor affecting the spacing between plants in the row is the forward speed. The size of seedling tray’s outlet opening significantly affects the number of seedlings per plant at the 99% confidence level, while planting depth affects it at the 95% confidence level.ConclusionGiven the recent water crisis, adopting the dry rice farming method and using transplanters offers a viable solution for managing and conserving water in agriculture. Implementing dry planting with a custom-made transplanter yields several benefits, including reduced water consumption, lower cultivation costs, improved soil aeration, increased efficiency, and simplified planting processes. Utilizing this transplanter is an effective strategy to decrease both the time and expenses related to transplanting, while also mechanizing rice planting in dry fields.
Research Article
Agricultural systems engineering (greenhouse, fish farming, mushroom production)
M. Bamdad; M. Zangeneh; S. H. Peyman
Articles in Press, Accepted Manuscript, Available Online from 21 April 2025
©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).
https://doi.org/10.22067/jam.2024.89685.1280
Abstract
IntroductionThe cooperative system for agricultural mechanization services holds significant potential to boost agricultural production. Key benefits include providing services during crop cultivation to reduce labor demands, delivering centralized and specialized services to lower production costs, ...
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IntroductionThe cooperative system for agricultural mechanization services holds significant potential to boost agricultural production. Key benefits include providing services during crop cultivation to reduce labor demands, delivering centralized and specialized services to lower production costs, facilitating the adoption of new technologies, and improving productivity in agricultural processes and supply chains. However, in Gilan Province, Iran, over 198 of the 260 registered mechanization service cooperatives, approximately 76%, are currently inactive. This significant decline highlights an urgent need to assess the performance of the mechanization service system to ensure its sustainability. Therefore, this study aims to identify the main challenges underlying the inactivity of these cooperatives in Gilan Province.Materials and MethodsThis study aims to evaluate the performance of mechanization service cooperatives in Gilan Province. To achieve this objective, a combined methodology incorporating Delphi methods, hierarchical analysis process, and the SWOT (strengths, weaknesses, opportunities, and threats) analysis was employed. Initially, factors influencing the performance of agricultural service cooperatives were identified through a review of existing literature concerning the performance pathology of agricultural cooperatives, as well as the specific factors impacting mechanization cooperatives and the associated challenges they face. The Delphi method was utilized to align the identified factors with the operational conditions of agricultural mechanization service cooperatives in Gilan Province. The Delphi process was conducted over three rounds, culminating in the identification and prioritization of the primary factors. Subsequently, the SWOT matrix was applied to assess the strengths, weaknesses, opportunities, and threats related to the mechanization service cooperatives, with input from a panel of experts for ranking purposes. Ultimately, strategies to address the performance challenges were developed based on the SWOT results and prioritized using AHP.Results and DiscussionThe findings of this research indicate that the lack of capital in the cooperative company, coupled with financial and credit difficulties, insufficient tools and equipment needed for generating income for members, stringent bank policies regarding the provision of financial aid, and the presence of discrepancies in the selection of cooperative members constitute the primary obstacles faced by these companies. The most important recommended strategies are as follows: Leverage internal investment from cooperative members. Attract capital from entrepreneurs. Organize tours to visit successful cooperatives for information exchange and learning. Reduce service fees. Increase access to financial assistance. Lower guarantee requirements for newly established cooperatives and entrepreneurs seeking financial supportConclusionIn this study, a comprehensive review of existing literature was conducted to identify the challenges affecting cooperative performance. These challenges were categorized into six main groups: financial, operational, organizational, structural, social, and environmental. The Delphi method, involving a panel of nine field experts, was used to evaluate these issues. Additionally, a SWOT analysis, based on previous research, was carried out to assess the strengths, weaknesses, opportunities, and threats related to cooperatives. To determine the relative importance of each challenge, a hierarchical analysis was performed to rank them accordingly.The results revealed that the most pressing challenges are primarily financial or have a significant financial impact on cooperative operations. Key issues include strict bank loan requirements, internal financial and credit difficulties, limited capital resources, and a lack of necessary tools and equipment to generate income for members. Moreover, the member selection process was identified as a critical concern, as it can lead to reduced motivation and inadequate fulfillment of responsibilities among cooperative members.
Research Article
Precision Farming
M. Safaeinezhd; M. Ghasemi-Nejad Raeini; M. Taki
Abstract
IntroductionOne of the key structural factors in agricultural mechanization is the selection of appropriate technology. Today, examining the effects of technology application and development on agricultural production remains of highly importance. Innovative technologies, such as spraying drones, play ...
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IntroductionOne of the key structural factors in agricultural mechanization is the selection of appropriate technology. Today, examining the effects of technology application and development on agricultural production remains of highly importance. Innovative technologies, such as spraying drones, play a critical role in advancing agriculture and ensuring food security. Without these technologies and proper input management, environmental impacts are likely to intensify. Achieving sustainable production and ensuring food security is a major challenge for researchers and global policymakers. This study evaluates and compares the performance of spraying drones and boom sprayers in controlling weeds and yellow rust disease in wheat fields. The aim of this study is to optimize pesticide use and achieve sustainable agriculture.Materials and MethodsThis research was conducted to evaluate the field performance and economic feasibility of using spraying drones compared to boom sprayers for controlling weeds and yellow rust disease in wheat fields. Experiments were carried out in regional Khorramabad, Iran, using a DJI Agras MG-1P spraying drone and a 400-liter 400B8 TF boom sprayer. The aim was to investigate the impact of modern technology, specifically spraying drones, compared to traditional methods, such as boom sprayers, for managing weeds and yellow rust disease. Additionally, the study assessed the profitability of these technologies. The experiments followed a randomized complete block design with three treatments: boom sprayer, spraying drone, and control. They were conducted in two separate, independent fields to examine treatment effects on weeds and yellow rust in wheat. For weeds control, 2-4-D herbicide was applied at 1.5 L ha-1, and for yellow rust control, Tilt fungicide was used at 0.5 L ha-1.Results and DiscussionResults showed that the deposition rate of pesticides in boom sprayers (82.8%) was higher than with drone spraying (69.9%). Furthermore, the average dry weight of weeds in boom sprayer was 172 g m-2, and in drone spraying, it was 163 g m-2, which was not statistically significant. Additionally, the average weed density was 25 plants per square meter for boom sprayers and 29.3 plants per square meter for drone spraying, with no statistically significant difference. The average harvest index in weed control experiments was 44% for boom sprayer and 41% for drone spraying, which was statistically significant at the 1% level. The average severity of yellow rust infection in wheat fields was 30.7% for boom sprayer and 25.3% for drone spraying, which was not statistically significant at the 1% level, but both treatments were significantly different from the control (68.3%). The harvest index in yellow rust experiments was better in drone spraying (43.8%) compared to boom sprayer (41.9%). The total annual cost for drone owners in the studied region (2980.3 million rials) was higher than the total cost for boom sprayer owners (513.48 million rials). However, the benefit-cost ratio for drone owners (1.215) exceeded that of boom sprayer owners (1.030), demonstrating economic viability for both sprayers. Overall, drones are found to be more economical for spraying than boom sprayers due to their higher efficiency and profitability. The use of drones can significantly increase the efficiency and profitability of spraying operations.ConclusionThe results of this study showed that both drone and boom sprayer were effective in reducing the dry weight of weeds, but there was no statistically significant difference between them. Weed density was higher with drone spraying, and the harvest index was better with drone spraying compared to boom sprayer. The costs of using drones were higher than boom sprayers, but despite the higher costs, drones are superior option for spraying due to their increased efficiency and profitability.
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.
