Journal of Researches in Mechanics of Agricultural Machinery

Abstract Abstract
Sugarcane harvesting is the most critical operation within sugarcane agro-industrial companies. Simultaneously with the harvest, sugarcane transportation is carried out using transport baskets (bins). In the case of sugarcane transportation, since the systems are mechanical, their reliability decreases as usage and age increase. Therefore, annual maintenance plays a vital role in enhancing the reliability of these transport machines. This research was conducted using Classic and Fuzzy GERT (Graphical Evaluation and Review Technique) methods at the Dehkhoda Sugarcane Agro-Industrial Company during the 2023-2024 crop year. The Work Breakdown Structure (WBS) and the GERT network model for the repair of sugarcane transport baskets were developed. Following the scheduling phase, the results obtained from the GERT method were compared with the actual time spent on system repairs to determine the accuracy of the estimates. The completion time for repairing a sugarcane transport basket was estimated at 102.44 hours using the Classic GERT method. However, using the Fuzzy GERT method, the calculated repair times were 140.06, 107.30, and 74.55 hours (representing the fuzzy intervals). Furthermore, the results indicated that 23.28% of the actual repair completion times fell outside the estimated fuzzy time range. The findings demonstrated that the resulting network model provides maintenance managers with a clear framework for making timely decisions, ensuring that the execution phase aligns with the planned schedule and operations are completed within the optimal timeframe.
EXTENDED ABSTRACT
Introduction
Sugarcane is one of the most important industrial products in Iran, serving as the primary source of sugar production and a key supplier of raw materials for numerous ancillary industries. It is only commercially cultivated in Khuzestan Province. The area under sugarcane cultivation in Khuzestan Province is 135,000 hectares. In sugarcane farming and industry companies, the most important operation is sugarcane harvesting, and this stage has a major contribution to the profitability or loss of these companies. In mechanical sugarcane harvesting, sugarcane fields are first set on fire to destroy the leaves, then the sugarcane is harvested with a harvester. At the same time as the harvesting operation, sugarcane is also transported by basket. The optimal and appropriate use of sugarcane carrying baskets, combined with a proper balance between the level of harvesting operations and the number of active machines, ensures that the operation starts and ends at the right time. This prevents losses due to delays in sugarcane harvesting and reduces additional costs. In the case of sugarcane transportation, since the systems in question are mechanical, the reliability of the system decreases with increasing use and also with its increasing lifespan. Therefore, annual repairs play a crucial role in enhancing the reliability of sugarcane carrying machines.
Material and Methods
This research was conducted using the classical and fuzzy Garrett method in Dehkhoda Sugarcane Cultivation and Industry Company in the 1402-1403 crop year. The work breakdown structure diagram and the Garrett network model of sugarcane basket repairs were drawn. In this research, after scheduling, the results obtained from the Garrett method were compared with the actual time spent on repairs of the sugarcane carrying system, and the fit of the estimates was determined. For this purpose, to determine the actual time required for repairs of the sugarcane carrying system, a sample was randomly selected from the sugarcane carrying baskets, and the actual duration of repairs in each was determined. Then, the actual time of completion of the operation was compared with the estimated time. The sugarcane carrying system in Dehkhoda Sugarcane Cultivation and Industry Company includes 45 18-ton baskets. In this research, to schedule the annual repairs of the carrying system, statistics and data related to the repairs of 25 sugarcane carrying baskets were collected. Data from other devices was used to measure the actual time of repairs against the calculated schedule.
Results and Discussion
The completion time of the sugarcane cart repairs was estimated to be 102.44 hours using the classic Garrett method. However, in the fuzzy Garrett method, the calculated times for sugarcane cart repairs were 140.06, 107.30, and 74.55 hours. The results also showed that 23.28 percent of the actual completion times of the cart repairs were estimated outside the fuzzy time. Solving the fuzzy lattice network for sugarcane basket repairs involves evaluating the network loops and the nodes. The outputs of this method include the operation completion time and the scheduling of other nodes in the network, which were obtained by performing calculations for node and end node evaluation. The operation completion time is a fuzzy number, and considering that the input factors of the network are triangular fuzzy numbers, the result of the operation completion time is also a triangular fuzzy number. That is, in fuzzy grid networks, the operation completion time is not a definite number, but rather a time interval and a fuzzy number that aligns with reality and helps eliminate tensions in the operation monitoring sessions. The fuzzy time for completing the maintenance of the basket is equal to T ̃_project=MT ̃_E=(74.55,107.30,140.06)(time unit: hours). Now, the maintenance manager has a time interval for completing the operation and can analyze it based on α slices. α can be considered as a risk level, and the decision maker can calculate and analyze the operation completion time interval at different risk levels. The lower (optimistic) and upper (pessimistic) limits of the α=0.5 cutoff for the basket repairs were obtained as 90.92 and 123.68 hours, respectively. The larger α is, the smaller the desired interval and the greater the accuracy. Therefore, the project manager can calculate and analyze the time interval required to complete the operation at different risk levels, and it is better to consider α as large. Additionally, the average time for basket repairs was determined to be 107.30 hours through defuzzification. However, the results obtained from the Garrett method in this study are close to the actual completion time of the operation, making the estimates more accurate. The actual duration of annual repairs for 20 baskets (out of a total of 45 baskets) at Dehkhoda Agriculture and Industry Company, which were repaired during the 1402-1403 crop year, was determined based on the information available in the company's mechanical equipment technical office. 23.28 percent of the actual completion times of basket repairs are estimated to be outside the fuzzy time.
Conclusions
The fuzzy Garrett method provides the repair unit manager with the opportunity to place the completion time in the estimated range appropriately, by considering other effective factors involved in the operation, so that there is no disruption in the implementation of activities and on the other hand, the costs of not completing the work on time are minimized. It also provides the possibility to create cuts at different time points and monitor the process of operation execution, modifying it as necessary. Finally, implementing and making the method practical and getting feedback, defining and determining the sugarcane machinery repair system and creating an information management system for them, examining the possibility of allocating resources and balancing the cost-time of sugarcane transportation system repairs with network models, and carefully and comprehensively examining the lags during work and the reasons for delays in operations in the sugarcane transportation system repair process and providing solutions to reduce them are suggested.
Acknowledgements
The authors would like to thank Shahid Chamran University of Ahvaz, for providing funding for this research.
Author Contributions
Mehdi Anafecheh: Data collection and processing
Nasim Monjezi: Supervision and management
Data Availability Statement
This section details where supporting data can be found, typically including links to publicly archived datasets. If no data is reported, "Not applicable" should be stated.
Ethical Considerations
This section states ethical approval details (e.g., Ethics Committee, ethical code) and confirms adherence to ethical standards, including avoidance of data fabrication, falsification, plagiarism, and misconduct.
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper
Funding Statement
The author(s) received no specific funding for this research

Abstract Abstract
Nowadays, due to the continuously increasing global demand for energy and the depletion of fossil resources, renewable energies have gained greater importance. Accordingly, investigating the feasibility of utilizing renewable energy sources in various conventional systems is of significant interest. The objective of this paper is to perform agent-based simulation, thermodynamic analysis, and exergy analysis of the integration of a supercritical gas turbine Brayton cycle with an organic Rankine cycle using biomass and solar energy in off-grid regions of Iran. In this study, the effects of various parameters—including temperature, compressor pressure ratio, and solar irradiation intensity—on the energy and exergy efficiencies of the system, the amount of heat generation in the solar collector, the input, output, and destroyed exergy of the system, as well as the total power output of the overall system, were investigated. The results indicate that with increasing compressor pressure ratio, the net power output and the energy and exergy efficiencies of the system initially increase by 4% and 5%, respectively, and then decrease, while the exergy destruction of the system increases continuously. Furthermore, the results show that the total input exergy to the system is 51,672 kW, whereas the rates of exergy destruction and exergy output from the system are 33,756 kW and 17,717 kW, respectively. The highest exergy destruction occurs in the solar collector, followed by the heat exchanger, while the lowest exergy destruction occurs in the pump.
EXTENDED ABSTRACT
Introduction
The use of solar radiation energy in gas turbine cycles is one innovative method to increase the efficiency of these cycles. The use of heliostat solar reflectors and the placement of a solar receiver upstream of the combustion chamber can increase the temperature of the air entering the combustion chamber, thereby reducing fuel consumption. The reduction of fuel injection into the combustion chamber, in turn, enhances efficiency, reduces pollution, and decreases the irreversibility rate in the cycle. In this system, the solar collector is installed at the top of a tower and absorbs the energy emitted from the solar panels installed on the ground surface. Compressed air is sent through pipes to the top of the tower, where it is heated in these collectors and then directed to the combustion chamber. Along the path between the compressor and the solar collector, due to the increase in air volume, the air temperature rises while its pressure decreases. In the path from the collector’s outlet to the combustion chamber, there is a slight decrease in temperature and pressure, which does not significantly affect the system's performance. Insulating the return air pipes from the tower can compensate for this temperature reduction to some extent. In cases of high solar radiation intensity, the combustion chamber can be completely removed, and a solar receiver can be used as an alternative. In this case, the system's efficiency will increase significantly, and the irreversibility rate will reach its minimum possible value.
Material and Methods
In this study, an effort was made to utilize biomass fuel to generate the heat required for the Brayton cycle. The proposed system consists of several subsystems. A brief description of the overall system and its components is provided below. Based on the advantages and findings of previous research on the supercritical Brayton cycle with CO₂ as the working fluid, this cycle was selected as the secondary cycle to recover waste energy from the heat generation system using biomass fuel.
Subsequently, the exhaust gases from the first turbine are reheated using a solar source and directed toward the second turbine to generate additional power. The exhaust gases from the second turbine are then directed to the Organic Rankine Cycle located at the end of the system.
Furthermore, considering the thermodynamic requirement for the main compressor inlet temperature in the S-CO₂ cycle to be low, a significant amount of energy is lost to the environment in this section. For the system analysis, a computer program was developed in EES software, and the thermodynamic and exergetic performance of the system was evaluated.
Results and Discussion
The power output of the gas turbine system and the net total power initially increase with the rise in the compressor pressure ratio, reaching a maximum at a pressure ratio of 6 to 7, and then decrease. Increasing the compressor pressure ratio leads to an increase in the power generated by the turbine. However, within the pressure ratio range of 6 to 20, the work consumed by the compressor increases, which results in a reduction in the system's power output. The variations in energy efficiency and exergy efficiency follow a trend similar to the changes in the system’s power output. As the compressor pressure ratio increases, both the energy efficiency and exergy efficiency of the system initially rise, reach a maximum point, and then begin to decrease. With an increase in the compressor pressure ratio, the exergy input to the system increases. As the compressor pressure ratio increases, more air enters the system, and consequently, more fuel is introduced for combustion, resulting in higher exhaust gas temperatures from the combustion chamber. These gases then enter the turbine, resulting in an increase in the power generated by the turbine. On the other hand, the exergy input to the system also increases with the rise in the compressor pressure ratio. With an increase in solar radiation intensity, the amount of heat input to the system rises, leading to an increase in the power generated by the S-CO₂ turbine and, consequently, an increase in the total power output of the system. As the system's power output increases, its energy efficiency also improves.
The rise in solar radiation intensity results in an increase in both the heat and exergy input to the system, which in turn enhances the overall exergy efficiency of the system. Therefore, increasing solar radiation intensity directly contributes to higher heat and exergy input to the system. As solar radiation intensity increases, the heat and exergy input to the system rise. As mentioned earlier, one of the components of the system that contributes to exergy destruction is the solar collector. The irreversibility in the solar collector leads to exergy destruction. With an increase in solar radiation intensity, the exergy input, exergy output, and exergy destruction also increase due to the higher heat input to the system. Additionally, as the temperature of the gases entering the turbine rises, the power generated in the Rankine cycle and the overall system power output also increase.
Conclusions
As the compressor pressure ratio increases, the exergy efficiency initially rises and then decreases. At a pressure ratio of 7, the exergy efficiency reaches its maximum value of 64.7%.
•With an increase in the compressor pressure ratio, the exergy input, exergy output, and exergy destruction also increase. For instance, the exergy input to the system rises from 48,000 kW to 55,000 kW.
When solar radiation intensity increases, both energy efficiency and exergy efficiency improve. Specifically, the energy efficiency increases from 53% to 57%, while the exergy efficiency rises from 64% to 69%.
•As solar radiation intensity increases, exergy destruction, along with the exergy input and output of the system, also grows. The exergy input to the system increases from 45,000 kW to 62,000 kW. Additionally, the amount of energy entering the system from the solar collector increases from 300 kW to 3,800 kW.
The highest exergy destruction occurs in the solar collector, followed by the heat exchanger, while the lowest exergy destruction takes place in the pump.
Author Contributions
Moez Behrouz: Methodology
Pourdarbani Raziyeh: Supervisor
Ghaebi Hadi: Validation
Data Availability Statement
"Not applicable"
Acknowledgements
The authors would like to acknowledge the Bu-Ali Sina University for finantcial support
Ethical Considerations
This section states ethical approval details (e.g., Ethics Committee, ethical code) and confirms adherence to ethical standards, including avoidance of data fabrication, falsification, plagiarism, and misconduct.
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper
Funding Statement
The author(s) received no specific funding for this research

Abstract Abstract
Soil compaction is a negative factor for plant growth. Subsoiling is a practice that improves the physical, chemical, and biological properties of soil below the conventional tillage depth, leading to increased crop yield and improved water and nutrient use efficiency. The objective of this study was to evaluate the effect of subsoiling on the yield of wheat (irrigated and rainfed), sugar beet, grain maize, and cotton by reviewing domestic studies and applying a meta-analysis approach. To this end, articles, research reports, and student theses published over the past three decades were reviewed using keywords related to subsoiling in the aforementioned crops. After quality screening, 18 studies were included in the meta-analysis using a random-effects model, comprising comparisons of subsoiling versus no subsoiling for wheat (13 studies under irrigated conditions and 5 under rainfed conditions), 8 studies for sugar beet, 4 studies for grain maize, and 5 studies for cotton. The data required for the meta-analysis included the treatment mean (X̄), standard deviation (SDₓ̄), and number of replications or sample size (n) in the experimental design. In data analysis, the standardized mean difference (SMD), one of the key effect size indices, was used to compare yield between the control treatment (no subsoiling) and the experimental treatment (with subsoiling). Based on the effect size values for irrigated wheat (SMD = +0.72), rainfed wheat (SMD = +1.14), sugar beet (SMD = +0.41), grain maize (SMD = +0.86), and cotton (SMD = +1.15), it can be concluded that subsoiling had a positive and significant effect on the yield of wheat (both irrigated and rainfed) and cotton, whereas its effect on sugar beet and grain maize was positive but not statistically significant. Compared with no subsoiling, subsoiling increased the yield of irrigated wheat, rainfed wheat, sugar beet, grain maize, and cotton by 13.27%, 16.02%, 3.74%, 5.84%, and 14.00%, respectively. Therefore, subsoiling is recommended to alleviate soil compaction and improve the yield of the aforementioned crops, particularly wheat and cotton.

EXTENDED ABSTRACT
Introduction
Agriculture is facing new challenges due to climate change, and food shortages are imminent. As a result, new sources of food and water will be needed. In agricultural fields, the subsoil (the layer below the plowed soil) can store approximately 50% of the total nitrogen and 25-70% of the total phosphorus. It can also retain water even in dry conditions. However, the availability of these resources varies between crops. Soil compaction is a negative factor for plant growth. To remove the dense soil layer, subsoiling has been introduced, which reduces soil resistance and facilitates deeper root penetration. Thus, plants have access to subsoil resources. Subsoiling improves the physical properties of the soil (reducing soil resistance, reducing soil bulk density, and increasing water infiltration into the soil) without turning the soil over. Subsoiling is a field measure to improve the physical, chemical, and biological properties of the soil below the common plowing depth to increase crop yields, water, and nutrient use efficiency. Subsoiling requires a significant amount of fuel and energy, as well as the use of powerful tractors. Therefore, before doing so, it is necessary to ensure that a hard layer (cone index greater than 2 MPa) is present, which reduces performance. Otherwise, the only result of subsoiling will be increased fuel consumption, wear and tear on the tractor and equipment, and destruction of the soil structure.
Material and Methods
The purpose of this research is to investigate the effect of subsoiling on the yield of wheat, sugar beet, corn, and cotton, using internal studies and research, and applying the meta-analysis method. For this purpose, articles, research reports, and student theses from the last thirty years were reviewed using keywords related to subsoiling in the mentioned products. Finally, after quality control, 18 studies in wheat, 8 studies in sugar beet, 4 studies in grain corn, 5 studies in cotton were entered into the meta-analysis process to compare subsoiling with using a random model. The data required for meta-analysis are the treatment mean((X)) ̅, standard deviation (SD_X ̅ ), and the number of replicates or sample size (n) in the experimental design. In data analysis, the standardized mean difference parameter, which is an important factor of effect size, was used to compare the performance between the control treatment (without subsoiling) and the experimental treatment (with subsoiling).
Results and Discussion
Considering the total SMD value (0.84+), it can be concluded that subsoiling had a positive and significant effect on wheat yield. Subsoiling significantly increased wheat yield in both irrigated conditions (SMD = 0.72+) and dryland conditions (SMD = 1.14+). The average total yield of wheat in the no-subsoiling and subsoiling methods was 4191.6 and 4707.4 kg/ha, respectively. After weighing each yield, the average weighted yield of the no-subsoiling and subsoiling methods was 207.1 and 234.6, respectively. Subsoiling in irrigated conditions increased wheat yield by 13.27% compared to no subsoiling. Subsoiling in dryland conditions also increased wheat yield by 16.02% compared to no subsoiling. Considering the effect sizes of sugar beet (SMD = +0.41), grain corn (SMD = +0.86), and cotton (SMD = +1.15), it can be concluded that subsoiling had a positive and insignificant effect on sugar beet yield, a positive and insignificant effect on grain corn yield, and a positive and significant effect on cotton yield. Subsoiling increased sugar beet yield by 3.74% compared to the no-subsoiling method. The average total sugar beet yield in the no-subsoiling and subsoiling methods was 55868 and 59067 kg/ha, respectively. After weighing each yield, the average weighted yield of the no-subsoiling and subsoiling methods was 7126 and 7393, respectively. Also, the subsoiling in the cotton crop increased its yield by 14%. The average cotton yields for the no-subsoiling and subsoiling methods are 2457.1 and 2806.8 kg/ha, respectively. After weighing the yields, the average weighted yields of the no-subsoiling and subsoiling methods were 497.9 and 567.6, respectively. Subsoiling increased the yield of grain corn by 5.84% compared to the no-subsoiling method. The average total yield of grain corn in the no subsoiling and subsoiling methods was 9850.5 and 10392.3 kg/ha, respectively. After weighing each yield, the average weighted yields for the no-subsoiling and subsoiling methods were 2474.9 and 2619.5, respectively. The probability level values in the Begg and Egger methods (for estimating publication bias) for wheat studies were 0.76 and 0.41, respectively. This suggests that publication bias does not exist in wheat crop research. Also, the probability level values in the Begg and Egger methods in sugar beet studies were 0.09 and 0.17, in corn studies 1 and 0.81, and in cotton studies 0.14 and 0.42, respectively, which can be concluded that publication bias (except in sugar beet) does not exist in the research on the mentioned crops.
Conclusions
The effect of subsoiling on wheat and cotton yield was positive and significant, whereas on sugar beet and grain corn yield, it was positive but insignificant. The positive effect of subsoiling on wheat, cotton, sugar beet and corn yields can be attributed to improving soil physical properties such as reducing soil resistance, reducing soil bulk density, increasing water penetration in the soil, increasing soil moisture (in dryland conditions), and expanding the depth of root penetration in the soil, which provides conditions for better plant growth. The greatest effect of subsoiling on wheat yield was in soils with silty clay loam texture. Subsoiling increased the yields of irrigated wheat, rainfed wheat, sugar beet, grain corn,

Abstract Abstract
Given that Persian shallot (mooser) is predominantly consumed in powdered form in the food industry, its drying is essential. Nowadays, due to the non-sterile conditions and low quality of products dried using the traditional sun-drying method, this approach is no longer employed, and industrial dryers are used instead. Among drying methods, the application of the impinging jet technique has attracted considerable attention from researchers in recent years. The advantages of this type of dryer include high heat transfer rates, reduced drying time, and improved quality of the final product. In the present study, the effects of air temperature at three levels (50, 60, and 70 °C), nozzle outlet air velocity at three levels (7, 10, and 13 m s⁻¹), and the ratio of nozzle-to-product surface distance to nozzle diameter (H/D) at three levels (4, 5, and 6) in an impinging jet dryer on the drying rate and color changes of Persian shallot slices were investigated. The experiments were conducted and analyzed using response surface methodology (RSM) based on a face-centered central composite design (CCD). The results showed that achieving a high drying rate occurred at higher air temperatures and velocities and lower H/D ratios. Moreover, the effects of temperature and air velocity on color change indices were significant, whereas the H/D ratio had no significant effect on these indices. The influence of air temperature on the drying rate of Persian shallot slices was greater than that of air velocity. The effects of air temperature and velocity on the color change indices ΔL* and Δb* were significant at the 1% level, while the H/D ratio had no significant effect on these indices. The optimal conditions for drying Persian shallot slices using the impinging jet dryer were determined to be an air temperature of 69 °C, an H/D ratio of 5.1, and an air velocity of 10.4 m s⁻¹.
EXTENDED ABSTRACT
Introduction
Allium hirtifolium is one of the most commonly used agricultural products in the food industry in Iran, because it is rich in minerals, vitamins, essential fatty acids, and antioxidant compounds. Due to the major consumption of powdered allium hirtifolium in products that may be stored on the shelf for weeks or months, drying is one of the essential steps in its processing to preserve it longer. Today, due to the non-sterility and low quality of the dried product resulting from the old method of using sunlight, this method is no longer used, and industrial dryers are employed to dry the products. There are numerous methods for drying agricultural products. Each method has some benefits and some limitations. Energy consumption of the process, drying time, quality of the final product, and available technology are the most important factors in selecting the method to be used for a specific product. Among the drying methods, the impact jet method has been considered by many researchers in recent years. A major part of the results that have been reported shows that this technique could be a suitable method for drying a wide range of agricultural products. The advantages of this type of dryer include high heat transfer, reduced drying time, and increased quality of the final product.
Material and Methods
In the present study, the relationship between some factors that can affect the drying process and the final reduction of humidity of Allium hirtifolium slices was studied. Also, some visual factors, such as the colour of the slices after drying time, were considered. The effect of air temperature at three levels of 50, 60 and 70 degreesCelsius, the velocity of the air coming out of the nozzles at three levels of 7, 10 and 13 meters per second and the ratio of the distance from the nozzle to the product surface to the nozzle diameter H/D Three levels 4, 5 and 6 in the collision jet dryer were investigated on the drying rate and color of allium hirtifolium leaves. The experiments were conducted using the response surface method and the central composite design in face-centered mode.
Results and Discussion
As expected, the air temperature of the dryer had a reverse relationship with drying time. With an increase in fluid temperature from 50° C to 70° C, the drying time was reduced from 95 minutes to 50 minutes. Of course, the relationship was not linear. At the first period of the drying process, in which the humidity content of the slices was higher, the water evaporation rate from the surface of Allium hirtifolium pieces was higher. Over time, the evaporation rate was reduced because of the reduction in the water content portion of the slices. Additionally, it was clarified that, although all three factors of air temperature, ratio of distance to diameter, and air velocity affected the drying process, the role of fluid temperature was the most significant. Increasing air velocity was also reduced during the period. At a constant 60° C temperature of fluid, by increasing the speed level of air from 7 ms^(-1) to 13 7 ms^(-1)Drying time was reduced from 90 minutes to 70 minutes. This relationship was not linear and had a more significant effect at lower speeds. The ratio of the distance from the nozzle to the product surface to the nozzle diameter, H/D, was another effective factor, the role of which on drying time was confirmed. When the H/D ratio was 4, the drying period time was 75 minutes. For an H/D ratio of 5, the drying period time was 80 minutes, and for an H/D ratio of 6, the drying period time was 90 minutes. So, it is clear that although the H/D ratio has an effect on the drying period time, its portion is minor. Another parameter that has been studied in this research is the final colour of Allium hirtifolium pieces. To quantify this parameter, three indices were used: (a*), (b*), and (L*). These indices were used to consider suitable situations and select the best combinations of effective factors in the drying process. To achieve final results and visualize complex data, the collected quantities of measured parameters were presented in 3D graphs, allowing for the analysis of holistic situations and effects. By studying these graphs, it can be seen that if one of the effective factors, such as the H/D ratio, were constant, another factor could affect the drying process by itself or in combination with the third one.
Conclusions
Many drying methods have been used by researchers in Iran to prepare Allium hirtifolium pieces as a widely used industrial product. Among them, the impact jet method is probably the best. Obviously, some factors, such as air temperature, air velocity, and H/D ratio, have a serious effect on the drying period time of the product and the quality of the final leaves. Finding the best combinations of these factors that yield the best outcome with minimum inputs is the big challenge that has been studied in this research. So many experiments were conducted to consider various situations. The results showed that high drying rates occur at high temperatures and air velocities, as well as low H/D ratios. The most effective factor was air temperature. Also, the effect of two variables, temperature and air speed, on the variation of color change indexes, 〖∆L〗^* and 〖∆b〗^* was significant, but H/D had no significant effect on these indices. Optimal conditions for drying Allium hirtifolium leaves with a jet dryer at 69 ° C, H/D ratio equal to 5.1, and air speed of 10.4 (m/s) were suggested.
Author Contributions
Esmaei Seidi: 34%
Aydin Salimi asl: 33%
Mohammad Hadi Mohammadi: 33%
Data Availability Statement
Not applicable.
Acknowledgements
Not applicable.
Ethical Considerations
This section states ethical approval details (e.g., Ethics Committee, ethical code) and confirms adherence to ethical standards, including avoidance of data fabrication, falsification, plagiarism, and misconduct.
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper
Funding Statement
The author(s) received no specific funding for this research

Abstract Abstract
The use of technologies based on site-specific management that enable effective weed control while causing minimal damage to the main crop is of great importance. In this study, color images and a deep learning strategy based on convolutional neural networks were employed to distinguish saffron from two of its common weeds, namely Azmak and Khakshir. A total of 291 images from the three classes (saffron, Azmak, and Khakshir) were captured under completely natural field conditions. The images were resized to 150 × 150 pixels, and data augmentation was then applied to virtually increase the number of images. The proposed deep learning architecture consisted of an improved VGG16 model, with initial weights applied using the transfer learning approach. Based on the obtained results, the accuracy of the proposed model on the test data was 91%, with an error rate of 0.3759. For the three target classes—saffron, Azmak, and Khakshir—the F1-scores were 85%, 100%, and 86%, respectively. The results also showed that the model achieved a precision of 100% in distinguishing saffron and Azmak from the other classes. This study can be considered a foundation for the development of robotic systems aimed at site-specific weed management in saffron fields.
EXTENDED ABSTRACT
Introduction
Precision agriculture technologies play a crucial role in optimizing crop management by enabling site-specific interventions, particularly in weed control. Traditional weed management methods often result in excessive herbicide use, environmental damage, and crop damage. Automated weed detection using deep learning offers a promising solution, accurately distinguishing between crops and weeds, thereby facilitating targeted removal. Saffron, a high-value crop, faces competition from invasive weeds such as Flixweed and Hoary Cress, which reduce yield and quality. This study leverages computer vision and deep learning to classify saffron and these two common weeds under natural field conditions. Convolutional Neural Networks (CNNs), were employed due to their proven effectiveness in image classification tasks. Transfer learning was applied to enhance model performance by utilizing pre-trained weights from ImageNet. The research aims to develop a robust classification model that can support precision agriculture tools, such as robotic weeders, by accurately identifying weeds while preserving the main crop. The success of this approach could significantly reduce herbicide use, lower production costs, and improve saffron yield through automated, site-specific weed management.
Material and Methods
A dataset of 291 field images of saffron, Flixweed, and Hoary Cress was collected under natural lighting and environmental conditions. Each image was resized to 150×150 pixels, and data augmentation techniques (e.g., rotation, flipping, scaling) were applied to expand the dataset and improve model generalization artificially. The study utilized the VGG16 CNN architecture, fine-tuned via transfer learning with ImageNet weights. The model was trained to classify the three plant categories, and its performance was evaluated using test data. Key metrics included accuracy, F1-score, and precision to assess classification effectiveness.
Results and Discussion
The proposed model achieved an overall accuracy of 91% on unseen test data, with a loss of 0.3759. The F1-scores for Saffron, Hoary Cress, and Flixweed were 85%, 100%, and 86%, respectively. Notably, the model demonstrated perfect precision (100%) in distinguishing saffron from Hoary Cress, indicating no false positives for these classes. Flixweed recognition was slightly less precise but still highly effective (86% F1-score). The high classification accuracy suggests that deep learning, combined with transfer learning, is a viable approach for weed detection in precision agriculture. The model's ability to differentiate saffron from invasive weeds under real-world conditions supports its potential integration into automated weeding systems. These results suggest that robotic weeders equipped with such AI models can selectively target weeds while minimizing crop damage, thereby reducing reliance on broad-spectrum herbicides.
Conclusions
This study demonstrates the effectiveness of deep learning in distinguishing saffron from Flixweed and Hoary Cress under natural field conditions. The improved VGG16 model achieved high accuracy (91%) and near-perfect precision for certain weed classes, validating its potential for real-world agricultural applications. The findings provide a foundation for developing AI-driven weed removal robots, which could enhance precision farming by enabling targeted, sustainable weed management. Future research should focus on optimizing models for real-time processing and integration with robotic systems. Expanding the dataset to include more weed species and varying environmental conditions could further improve robustness. Overall, this work contributes to advancing precision agriculture technologies, offering a scalable solution for automated weed control in saffron fields and similar high-value crops.
Author Contributions
S.I. Saedi: Software, Methodology, Modeling, Writing of the initial draft, Final review, and editing.
H.Makarian: Review and editing of the text, Research, Data collection, Validation.
Data Availability Statement
Not applicable.
Acknowledgements
Not applicable.
Ethical Considerations
This section states ethical approval details (e.g., Ethics Committee, ethical code) and confirms adherence to ethical standards, including avoidance of data fabrication, falsification, plagiarism, and misconduct.
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper
Funding Statement
The author(s) received no specific funding for this research

Abstract Maintaining an appropriate storage temperature for agricultural products, especially fruits and vegetables, is crucial for minimizing quality deterioration and waste. In the realm of high-spatial resolution temperature monitoring for the cold chain of perishable food items, the most popular solution involves temperature estimation methods. This study aimed to develop a “one-sensor-per-pallet” soft temperature sensor for fruit pallets using a novel supervised feature-selection technique to select and rank its inputs. Additionally, the reliability of the ranking obtained from this feature-selection method was evaluated using a meta-heuristic algorithm (Ant-Colony Optimization). To collect the necessary data, a data logger was developed based on Arduino technology, enabling real-world data collection of temperature values at various points of peach fruit pallets in a cold storage facility. Time-Delay Neural Networks were considered as the estimator algorithm for the soft sensor system. Evaluation of the developed soft sensor which was performed using a new dataset, showed that the average RMSE of its estimates at various points of the pallet, situated in entirely different temperature scenarios and locations within the cold facility, ranged between 0.8 and 0.1 K. These results appear favorable in the “training-under-only-one-scenario” condition. Results indicate the high potential of supervised feature-selection as a structured objective method for determining the optimal positions of the physical sensors required. However, the average RMSE of the position selected by the meta-heuristic algorithm was significantly lower for the “one-sensor-per-pallet” mode. Therefore, it is recommended to explore other methods as well when reducing the number of physical sensors.

Introduction
Temperature is a critical factor affecting the quality and shelf life of perishable food products, making its precise and cost-effective monitoring essential throughout the cold chain. Due to practical limitations in deploying physical sensors at every point of interest, the development of soft (virtual) sensors for temperature estimation has attracted considerable research attention. Soft sensors are generally categorised into model-based and data-driven approaches, with the latter offering superior capability in capturing system dynamics without requiring detailed physical modelling. Among various data-driven methods, artificial neural networks (ANNs) have demonstrated superior performance compared to alternative techniques.
The first step in designing a data-driven soft sensor is to collect and prepare training data, which can be either experimental or synthetic. Studies indicate that experimental data lead to more accurate estimations, although they are harder to obtain. A crucial subsequent step involves selecting appropriate input variables corresponding to potential sensor locations. While most previous studies have relied on subjective or heuristic input selection, supervised feature selection methods offer a structured and potentially more optimal alternative; however, such methods have rarely been applied in the context of temperature soft sensors for cold chains.
Once inputs and outputs are defined, the modelling process typically employs ANNs, which have shown superior estimation performance over classical models. The present study implements supervised feature selection to optimise both the number and placement of physical sensors and to statistically investigate the effects of key hyperparameters in the time-delay neural network (TDNN) serving as the system's soft sensor estimator.

Materials and Methods:
A cost-effective, Arduino-based data logger was developed to collect real temperature data for training the soft sensor. Temperature distribution data were gathered from two peach pallets stored in a 2000-tonne cold storage room maintained at 1 °C. The logger simultaneously recorded data from up to 20 DS18B20 sensors at 1-minute intervals. A novel supervised feature selection approach (wrapper method) was applied to identify the most informative sensor positions, thereby minimising the required number of physical sensors. A time-delay neural network (TDNN) with one hidden layer was employed for temperature estimation. The influence of four key hyperparameters—time delay, number of hidden neurons, activation function, and training algorithm—on model performance metrics (RMSE, R², MAE, BIC) was evaluated. Additionally, a metaheuristic algorithm assessed the effectiveness of the feature selection method in reducing sensor count. Model generalisability was verified using an out-of-sample test set collected under varying temperature scenarios and pallet placements.

Results and Discussion:
Data acquisition for the first pallet lasted five hours, during which the cold storage refrigeration system operated under standard conditions. Table 1 ranks the five most informative sensor locations identified using the FIRE method. Table 3 presents the best model fits obtained with the SIG and ReLU activation functions. These models share a common structure: Levenberg–Marquardt (LM) training and a 20-minute time delay. Comparing activation functions reveals that ReLU yields superior performance metrics (R²mean and RMSEmean), while SIG achieves a lower BIC. Figure 4 illustrates how R²mean and RMSEmean vary as the number of physical sensors is reduced based on FIRE rankings under the optimal estimator configuration (LM2010-ReLU), showing no consistent trend and indicating the robustness of FIRE’s variable selection.
For the second pallet, data acquisition also spanned five hours; however, the refrigeration system was inactive for defrosting during the first three hours and resumed operation in the final two. Table 2 shows the FIRE-derived variable ranking, and Table 4 lists the best model fits for this dataset. All models for the second pallet employed LM training, a 20-minute delay, and five hidden neurons. In this case, performance metrics (R²mean and RMSEmean) progressively deteriorated as fewer sensors were used (Figure 4), suggesting greater sensitivity to sensor reduction under dynamic thermal conditions.
Correlation analysis revealed strong relationships between RMSEmean and MAEmean, as well as among MSEtrain, MSEvalidation, and MSEtest, whereas R²mean showed little correlation with these. BIC exhibited significant correlation with several metrics. Consequently, MSEtrain, R²mean, and RMSEmean were deemed sufficient for performance evaluation.
The influence of estimator hyperparameters (Table 5) indicated that training method significantly affected performance, favouring LM. Hidden layer size and time delay primarily influenced BIC, while activation function showed no significant effect, consistent with Loisel et al. (2022).
Ant Colony Optimisation (ACO) was applied to validate FIRE rankings (Figure 5). ACO results revealed alternative sensor combinations yielding lower RMSEmean than FIRE, suggesting that heuristic methods may offer superior configurations for “one-sensor-per-pallet” setups. Table 6 compares FIRE and ACO outcomes, confirming ACO’s advantage in reducing RMSEmean.
Cross-pallet validation demonstrated that the soft sensor trained on one pallet and tested on the other under different thermal scenarios achieved RMSEmean values of 0.8 K and 1.0 K—13–15 times higher than training RMSEmean but acceptable given scenario differences. However, R²mean (~0.4) indicated limited generalisation, implying that future training with diverse scenarios would enhance robustness (Figure 6).

Conclusions
The findings of this study demonstrate the high potential of supervised feature selection as a structured and objective method for identifying the most informative locations for hardware sensor placement in the development of soft sensors. However, to further reduce the required number of hardware sensors, the use of alternative methods, such as metaheuristic algorithms or exhaustive search techniques, appears to be a more prudent approach. In this regard, future research could explore and compare different supervised feature selection techniques to assess their ranking effectiveness and robustness in similar applications.
The out-of-sample evaluation of the developed soft sensor in the “one-pallet-one-sensor” configuration revealed RMSEmean values ranging between 0.8 K and 1.0 K. This outcome is considered acceptable given the “single-scenario training” condition. Nevertheless, several factors could influence the accuracy and generalisation performance of the soft sensor, including: (1) the size of the dataset (in terms of duration or number of data points), (2) the temperature scenario or the operational state of the refrigeration system, and (3) the location of the pallets within the cold storage. Therefore, increasing the diversity of data acquisition conditions—such as varying spatial placement or thermal scenarios—is likely to yield valuable information for further improving the soft sensor’s performance and predictive accuracy.

Abstract Abstract
Changes in agricultural land use driven by human or natural factors and their monitoring constitute one of the most important challenges in the northern provinces of the country. Different machine learning algorithms exhibit varying performance in regions with specific spatial characteristics. The aim of the present study was to use Sentinel-2 satellite imagery to classify land use in Masal County and to compare the maximum likelihood, minimum distance, Mahalanobis distance, and support vector machine classifiers with linear, polynomial, sigmoid, and radial basis function kernels.
To provide training and testing data for the algorithms, in addition to ground truth points collected using a global positioning system (GPS) and Google Earth maps, spectral indices—including the normalized difference vegetation index (NDVI), the normalized difference built-up index (NDBI), and the normalized difference water index (NDWI)—were used to more accurately determine ground truth information. Model validation was performed using a confusion matrix and by calculating the kappa coefficient and overall accuracy. The results showed that the maximum likelihood algorithm achieved the most accurate classification performance, with an overall accuracy of 97.29% and a kappa coefficient of 0.96, while the support vector machine with a linear kernel ranked second, with kappa coefficient and overall accuracy values of 0.89 and 92.46%, respectively. The weakest performance was observed for the minimum distance algorithm, with an overall accuracy of 72.97% and a kappa coefficient of 0.66. Based on the results of this study, the maximum likelihood classifier provided superior performance in discriminating land use classes in Masal County. The findings of this research are of significant importance for policymakers and provincial managers in increasing awareness of changes in agricultural and forest land use in the region.

EXTENDED ABSTRACT
Introduction
Land use and land cover change, driven by either anthropogenic activities or natural dynamics, is a critical environmental challenge in many regions of the world, particularly in ecologically sensitive and agriculturally significant areas such as the northern provinces of Iran. Monitoring these changes is essential for sustainable resource management, environmental protection, and regional planning. Masal County, with its diverse landscape including forested areas, agricultural lands, and urban settlements, represents a case where accurate and timely land use classification is vital for informed decision-making. Remote sensing technologies, particularly multispectral satellite data like Sentinel-2, have emerged as powerful tools for capturing spatial and temporal variations in land use. The application of machine learning algorithms has further enhanced the potential for precise classification; however, the performance of these algorithms can vary significantly depending on the spatial characteristics of the study area. Thus, selecting an appropriate classification method is crucial for reliable land cover assessment. This study aims to evaluate and compare the performance of four common classification algorithms, Maximum Likelihood, Minimum Distance, Mahalanobis Distance, and Support Vector Machine (SVM) with various kernel functions (linear, polynomial, sigmoid, and radial basis function), for classifying Land use and land cover in Masal County using Sentinel-2 imagery.
Material and Methods
The methodology of this study involved the acquisition and processing of Sentinel-2 satellite imagery covering Masal County. Ground truth data were established through a combination of GPS-based field surveys and interpretation of high-resolution Google Earth imagery. To enhance the accuracy of land use classification, several spectral indices were calculated, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI). These indices helped in distinguishing between vegetation, built-up areas, and water bodies, respectively. Four classification algorithms were tested: Maximum Likelihood, Minimum Distance, Mahalanobis Distance, and SVM with linear, polynomial, sigmoid, and radial basis function (RBF) kernels. Model validation was conducted using confusion matrices, with overall accuracy and Kappa coefficient serving as the primary evaluation metrics.
Results and Discussion
The comparative evaluation of the four classification algorithms Maximum Likelihood, Minimum Distance, Mahalanobis Distance, and Support Vector Machine (SVM) with various kernels revealed substantial differences in their ability to accurately classify land use types in Masal County using Sentinel-2 imagery and ancillary spectral indices. Among the tested methods, the Maximum Likelihood classifier demonstrated the highest performance, achieving an overall accuracy of 97.29% and a Kappa coefficient of 0.96. This high accuracy indicates the algorithm's strong capability in modeling the statistical distribution of pixels for each land use class, particularly when sufficient and representative training data are available. The confusion matrix for this classifier revealed minimal misclassification between major land cover types, including agricultural lands, forests, built-up areas, and water bodies. The high values for both overall accuracy and Kappa coefficient suggest that the Maximum Likelihood approach is robust for the heterogeneous landscape of Masal County. The SVM classifiers, especially with a linear kernel, also performed well, with an overall accuracy of 92.46% and a Kappa coefficient of 0.89. This suggests that the land cover classes in the study area are largely linearly separable in the feature space constructed from the spectral bands and indices. The SVM with other kernels polynomial, sigmoid, and radial basis function (RBF) showed slightly lower accuracies, indicating that increased model complexity did not necessarily translate to better discrimination in this particular dataset. The linear kernel's strong performance highlights its suitability for remote sensing applications where class boundaries are relatively straightforward. The Mahalanobis Distance classifier achieved moderate results, with overall accuracy and Kappa values lower than those of Maximum Likelihood and SVM, but higher than those of Minimum Distance. This method benefited from considering the covariance among spectral features, which helped in distinguishing classes with overlapping spectral signatures. However, its performance was limited in areas where class distributions were not well separated. The Minimum Distance classifier exhibited the weakest performance, with an overall accuracy of 72.97% and a Kappa coefficient of 0.66. Its reliance on simple Euclidean distances made it less effective in capturing the spectral complexity of the region, leading to higher rates of misclassification, particularly between agricultural and urban classes. Incorporating spectral indices such as NDVI, NDBI, and MNDWI significantly improved the classification results for all algorithms. These indices enhanced the separability of vegetation, built-up, and water classes, reducing confusion and increasing the reliability of the classified maps.
Conclusions
The findings of this study underscore the crucial role of algorithm selection in remote sensing-based land-use classification. Among the tested methods, the Maximum Likelihood classifier consistently delivered the highest accuracy and reliability for distinguishing land use types in Masal County, followed closely by the SVM with a linear kernel. The use of spectral indices further enhanced the classification results, emphasizing the value of integrating ancillary data for improved land cover discrimination. These outcomes offer practical guidance for land managers, policymakers, and researchers seeking to monitor and manage land-use changes in northern Iran and similar regions. Accurate land use maps are essential for tracking agricultural expansion, forest degradation, and urban growth, all of which have significant implications for environmental sustainability and regional planning. Future research should explore the integration of additional data sources and advanced machine learning techniques to refine classification accuracy further and support dynamic land use monitoring.
Author Contributions
Momeni Masouleh Seyed Abdollah: Data collection, Software, Visualization, Original Draft
Rahimi-Ajdadi Fatemeh: Conceptualization, Methodology, Validation, Writing - Review & Editing, Supervision, Project administration
Data Availability Statement
This section details where supporting data can be found, typically including links to publicly archived datasets. If no data is reported, "Not applicable" should be stated.
Acknowledgements
The authors would like to appreciate the support of the University of Guilan.
Ethical Considerations
This section states ethical approval details (e.g., Ethics Committee, ethical code) and confirms adherence to ethical standards, including avoidance of data fabrication, falsification, plagiarism, and misconduct.

Abstract Abstract
Reducing losses of agricultural products provides a valuable opportunity to enhance food security without the need for additional production resources. Each year, a considerable portion of cereal crops is wasted due to a lack of knowledge, unintentional management practices, low technical efficiency, and inadequate infrastructure. Losses occur in two forms: quantitative and qualitative. Quantitative losses manifest as reductions in volume and amount, whereas qualitative losses appear as decreased marketability and lower selling prices. In developed countries, the highest level of cereal losses occurs at the consumption stage, whereas in developing countries, losses mainly take place at on-farm stages. Mechanized harvesting plays a fundamental role in reducing losses, and through proper and engineering-based adjustment, losses can be minimized to the lowest possible level. Combines are used to accelerate wheat harvesting operations and reduce losses, with an acceptable cumulative loss of up to 7% across their components. Accordingly, in the present study, combine harvester losses were investigated at three stages—pre-harvest, during harvest, and post-harvest—in five counties in the southern part of West Azerbaijan Province. The analytic hierarchy process (AHP) was employed to prioritize the factors contributing to losses. The most important factor influencing increased losses was the transportation system (weight = 0.222), while climatic conditions (weight = 0.011) were identified as the least important factor. Among the studied counties, Miandoab (weight = 0.484) and Takab (weight = 0.072) ranked first and last in priority, respectively. Based on the magnitude of losses associated with different factors, the total loss was calculated to be 42.43% of wheat production in the previous cropping year, which can be reduced through proper supervision and the implementation of the proposed management strategies.
EXTENDED ABSTRACT
Introduction
Reducing agricultural losses presents a valuable opportunity to enhance food security without requiring additional production resources. A significant portion of grains is wasted annually due to a lack of knowledge, unconscious management practices, low technical efficiency, and inadequate infrastructure. Losses occur in both quantitative and qualitative forms, which are quantitatively expressed in terms of volume and quantity and qualitatively expressed in terms of marketability and low sales rates. In developed countries, the highest amount of grain losses occurs in the consumption sector, while in developing countries, they occur mainly in the in-field stages. Mechanized harvesting plays a key role in reducing losses, and with systematic regulation and engineering, the amount of losses will be minimized. Combine harvesters are used to accelerate wheat harvesting and reduce losses, and the accumulation of losses of each of its components is acceptable up to 7%. For this purpose, in this study, the amount of combine harvester losses was examined in three stages before harvesting, during harvesting, and after harvesting in 5 southern counties of West Azerbaijan Province. The analytic hierarchy process was used to prioritize the factors involved in losses. The most important parameter in increasing losses is related to the transportation system (weight = 0.222), and the weather factor (weight = 0.011) was the least important. Among the studied counties, Miandoab (weight = 0.484) and Takab (weight = 0.072) were assigned the highest and lowest priority, respectively. Considering the loss rates of different factors, the total loss percentage was calculated to be 42.43 percent of the wheat production in the previous crop year, which can be reduced with monitoring and the proposed solutions.
Material and Methods
Population expansion and the disparity between food supply and demand are significant issues, with an anticipated 9.7 billion individuals inhabiting Earth by 2050 (Østergaard et al., 2024). To address this issue, it may be necessary to reduce food waste, particularly concerning grains, which constitute 30% of total food loss (FAO, 2022). The primary causes of on-farm losses are pests, adverse weather conditions, poor agricultural practices, and insufficient financial resources (Mesterházy et al., 2020).
Shewry and Hey (2015) assert that wheat is a significant cereal crop that provides calories and essential micronutrients. Nath et al. (2024) assert that reducing post-harvest losses in wheat production can significantly enhance food security and economic stability. The wheat supply chain comprises numerous stages, both on-farm and off-farm, each of which may exhibit diminished profitability or productivity (Ekepu et al., 2017; Nawi et al., 2010). Iran's unsustainable practices, such as the exclusive cultivation of wheat and excessive dependence on agrochemicals, have exacerbated environmental degradation and health hazards (Hashemi Nejad et al., 2020).
This study employs the Analytic Hierarchy Process (AHP) to ascertain the principal factors leading to wheat post-harvest losses in the southern areas of Iran's West Azerbaijan Province and to devise economic strategies for their alleviation.
Results and Discussion
The southern counties of West Azerbaijan Province, namely Bukan, Takab, Shahin Dej, Miandoab, and Mahabad, are pivotal in wheat production owing to their fertile soil and conducive climate. However, there are losses that happen between planting, harvesting, and delivering the product to customers. Given the size of the production, these losses add up to a lot.
This study carefully looks at the reasons for these losses and, with the help of farmers and agricultural experts, comes up with effective and specific ways to resolve the problem.
Field and desk research conducted in these countries identified the causes and percentages of losses at each stage. Thereafter, the Analytic Hierarchy Process (AHP) was utilized to conduct pairwise comparisons among factors, identifying the most significant causes and the highest loss rates in wheat production. Consequently, requisite corrective actions were advised. The study also identified the counties with the greatest and least wheat losses based on the analyzed factors. The viewpoints of agricultural engineers, cooperatives, farmers, and combine harvester proprietors concerning loss factors, their root causes, and suggested remedies are delineated in the subsequent sections. Expert Choice software enables decision-making via the Analytic Hierarchy Process (AHP). The Analytic Hierarchy Process (AHP) relies on pairwise comparisons and is termed a hierarchical methodology, as it commences with an organization's objectives and strategies, subsequently broadening to discern and assess diverse decisions.
The Analytic Hierarchy Process (AHP) is a mathematical and subjective methodology designed to evaluate intricate issues across various tiers. Field and operational investigations examined several key factors contributing to increased losses in all studied counties. The factors influencing product losses, as documented through expert and farmer feedback, were analyzed and assessed utilizing the AHP method.
Conclusions
The consistency ratio must be maintained at or below 0.1 to obtain valid results from the Analytic Hierarchy Process (AHP). The pairwise comparison methodology in this study demonstrated an optimal level of precision and consistency, as evidenced by a calculated consistency ratio of 0.04. The transportation system was the most significant factor in causing more losses, as indicated by the analysis of expert opinions and recorded data (weight = 0.222). The weather, transportation system, and type of combine harvester were the first and second most significant factors, collectively accounting for 0.425 (42.5%) of the total losses. Given that these two factors account for over 40% of the total losses, they necessitate special consideration when developing strategies to mitigate them.
The countries that were examined were compared based on specific factors that resulted in a higher number of losses. Among the neighboring counties, Miandoab County (weight=0.484) possessed the most favorable conditions for wheat cultivation. In second place was Bukan County (weight=0.278).
This implies that these counties are superior to others due to their superior locations and increased resources. Organizing training workshops and granting agricultural extension officials more authority could prevent numerous crop losses. On the other hand, Takab County's isolation from the provincial center and other counties puts it at a disadvantage. For instance, it has a reduced number of agricultural service centers and combine harvesters. Consequently, it had the lowest net wheat yield in relation to the area in which it was cultivated.
Author Contributions
Himan Khodakam: Conceptualization; Writing
Razieh Pourdarbani: Supervision; Revision
Ehsan Bagheri: Data Collection; Methodology
Data Availability Statement
"Not applicable"
Ethical Considerations
It is confirmed the avoidance of data fabrication, falsification, plagiarism, and misconduct.
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper
Funding Statement
The author(s) received no specific funding for this research