Journal of Researches in Mechanics of Agricultural Machinery

Abstract Abstract
Fossil fuels such as diesel have consistently played a key role in energy production. Owing to the nonrenewable nature of fossil fuel resources and the pollution resulting from their combustion, numerous environmental problems have emerged. Therefore, the use of clean, renewable, safe, and cost-effective energy sources capable of meeting global energy demands is of vital importance. Research has shown that renewable fuels alone are not suitable for use in diesel engines; consequently, the blending of multiple fuels has attracted increasing attention. In the present study, the simultaneous effects of a quaternary fuel blend (diesel, biodiesel, bioethanol, and ammonia) on the performance and emissions of a single-cylinder direct-injection diesel engine were investigated, and the response surface methodology (RSM) was employed as an advanced statistical approach to optimize fuel and engine operating parameters. The results indicated that the optimal blend, consisting of 40% diesel, 20% biodiesel, 20% bioethanol, and 20% ammonia, was able to reduce CO and HC emissions by 65% and 60%, respectively, although NOx emissions increased by 21%. Furthermore, engine performance decreased by 23% as the diesel fraction in the fuel blend was reduced from 100% to 40%; however, the addition of ammonia partially compensated for this reduction. This study highlights the importance of utilizing various combinations of alternative fuels for application in internal combustion engines.
EXTENDED ABSTRACT
Introduction
Energy is a fundamental need of modern society, traditionally supplied by fossil fuels such as diesel. However, the non-renewable nature of fossil resources and their environmental impacts, including greenhouse gas emissions and particulate matter, necessitate the development of sustainable alternatives. Biofuels, such as biodiesel and bioethanol, have been widely investigated as renewable and oxygenated fuel sources. Biodiesel improves combustion and reduces CO and HC emissions, but suffers from high viscosity and poor low-temperature properties. In contrast, bioethanol enhances cold flow properties and fuel stability, making their blends with diesel more attractive. Ammonia has recently emerged as a promising carbon-free fuel due to its high hydrogen content, storability, and potential to reduce CO₂ emissions significantly. Despite challenges such as toxicity, slow combustion, and increased NOx emissions, ammonia can be used in dual- or multi-fuel strategies to optimize performance and emissions.
Several studies have demonstrated that blending biodiesel, bioethanol, and ammonia with diesel can improve combustion efficiency, lower particulate and CO₂ emissions, and reduce dependence on fossil fuels. Additives such as TiO₂ nanoparticles further enhance combustion and catalytic activity, contributing to lower fuel consumption and pollutant formation.Nevertheless, most prior research has focused on binary or ternary blends, while limited work has explored quaternary mixtures of diesel, biodiesel, bioethanol, and ammonia in compression ignition engines. In this context, this study applies response surface methodology (RSM) to evaluate and optimize the combined effects of these fuels on engine performance and emissions, addressing both energy security and environmental sustainability.
Material and Methods
Tests were conducted on a single-cylinder, naturally aspirated Mitsubishi NM45 diesel engine (operating at 3000 rpm, water-cooled, with direct injection). The fuels included commercial diesel, biodiesel from sunflower oil via transesterification, bioethanol, and liquefied ammonia synthesized by the Haber process. Blends of diesel (40–100%), biodiesel (0–40%), bioethanol (0–20%), and ammonia (0–20%) were examined at engine loads of 25, 50, and 75%. The engine was coupled to a hydraulic dynamometer for torque and power measurement, while a digital tachometer controlled speed.
A Testo 330 gas analyzer measured exhaust emissions, and ammonia flow was regulated by a Krohne DK 800 R flowmeter. Data acquisition was performed through a PLC-based monitoring unit (Atech/Delta 10SXR). Before each test, the system was flushed with the target fuel, and measurements were taken under steady-state conditions. Each experiment was repeated three times. Response surface methodology (RSM) with a mixture design was employed to model and optimize performance and emissions.
Results and Discussion
The statistical analysis of variance (ANOVA) demonstrated that the combined effects of fuel type, engine load, and ammonia addition significantly influenced emissions of CO, NOx, and HC. Most interactions among diesel, biodiesel, bioethanol, ammonia, and engine load were significant at the 1% or 5% levels, confirming the strong dependence of emissions on both fuel composition and operating conditions. The regression models developed for each pollutant exhibited excellent predictive performance, with R² values of 0.9961 for CO, 0.9959 for NOx, and 0.9635 for HC, indicating close agreement between predicted and experimental data. Lack-of-fit was not significant, further supporting the adequacy of the models.
For CO emissions, the lowest level (146 ppm) was observed at 25% load when the engine operated on a mixture of 40% diesel, 40% biodiesel, 20% bioethanol, and 20% ammonia in the intake air. In contrast, the highest CO level (423 ppm) was recorded with pure diesel and no ammonia. The reduction of CO by up to 65% under blended fuel conditions was attributed to the oxygenated nature of biodiesel and bioethanol, as well as the carbon-free structure of ammonia, which collectively promoted more complete combustion. At higher loads (75%), CO emissions further decreased, reaching a minimum of 86 ppm under conditions of high ammonia and low diesel fractions, due to improved combustion completeness.
In the case of NOx, emissions increased with higher shares of biodiesel, bioethanol, and ammonia. The maximum value (233 ppm) occurred at a 75% load with a blend of 40% diesel, 40% biodiesel, 20% bioethanol, and 20% ammonia. These increases were attributed to higher combustion temperatures, oxygen availability, and the nitrogen content of ammonia. Load was the most dominant factor, as increasing the load from 25% to 75% resulted in a 49% increase in NOx.
For HC emissions, the lowest concentration (50 ppm) was achieved with a mixture of 40% diesel, 40% biodiesel, 20% bioethanol, and 20% ammonia at a 75% load. Oxygenated fuels enhanced combustion efficiency and reduced HC emissions by 50–60% compared with pure diesel. However, load variations also played a key role, with higher loads significantly reducing HC due to improved combustion conditions.
Regarding engine performance, the maximum torque (14.34 Nm) and power (3.3 kW) were observed at a 75% load with an 80% diesel, 20% biodiesel, and 20% ammonia blend. While replacing diesel with renewable fuels reduced performance by ~23%, ammonia addition slightly improved it (~5%). These findings highlight that optimized fuel blends can reduce emissions without significantly compromising performance.
Conclusions
This study investigated the effects of diesel, biodiesel, bioethanol, and ammonia blends on the performance and emissions of a single-cylinder diesel engine. Using Response Surface Methodology (RSM), the influence of fuel composition and engine load was optimized and analyzed. Results showed that the blend of 40% diesel, 40% biodiesel, 20% bioethanol, and 20% ammonia achieved the lowest HC (50 ppm) at 75% load and reduced CO emissions by 65% at 25% load compared with pure diesel. However, this blend produced the highest NOx (233 ppm), while pure diesel at 25% load yielded the lowest (98 ppm). Replacing diesel with biofuels decreased power by approximately 23%, although the addition of ammonia improved performance by nearly 5%. The maximum power (3.3 kW) and torque (14.3 N·m) were achieved with a 80% diesel, 20% biodiesel, and 20% ammonia blend at 75% load. Higher loads increased NOx but reduced HC and CO, indicating more complete combustion.
Acknowledgement
The authors would like to express their gratitude to the Research Council of Shahrekord University for financially supporting this study under grant number 96GRN31M1007.
Author Contributions
Abolfazl Ziaadini: Methodology
Sajjad Rostami: Supervisor
Bahram Hossinzade Samani: Validation
Mostafa Kiani: Validation
Data Availability Statement
"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
This work was supported by Research Council of Shahrekord University under grant number 96GRN31M100

Abstract Abstract
With the continuous growth of the population, the need for sustainable food supply has become increasingly important. One of the approaches to enhancing productivity in greenhouse production is the use of advanced machine learning methods for analyzing greenhouse data. In this study, in order to predict the yield of greenhouse crops at the national level in Iran, statistical data from the Ministry of Agriculture Jihad were used, including information on provinces, counties, crop types, cultivated area, and production volumes. After data preprocessing, four machine learning models—multilayer perceptron neural network (MLP), support vector regression (SVR), decision tree (DT), and random forest (RF)—were applied. The models were evaluated using a split of 90% training data and 10% testing data. The predictive accuracy of the models was assessed using various statistical criteria and a one-way analysis of variance (ANOVA) test. The results showed that the RF model, with an R² of 0.9598 and an MSE of 15.6 × 10⁶, provided the most accurate predictions and was capable of modeling complex patterns among the data; however, this model had a relatively large size. Despite some errors at specific points, the DT model, with an R² of 0.8864 and an MSE of 44.3 × 10⁶, showed no statistically significant differences according to the statistical test and produced predictions close to the actual values. In contrast, the SVR and MLP models, with R² values of 0.8546 and 0.8878, respectively, exhibited significant differences between predicted and actual results. These findings emphasize that the proper selection of an algorithm consistent with data characteristics plays a crucial role in agricultural decision-making. The RF model introduced in this study represents a suitable framework for predicting greenhouse crop yields at the national level.
EXTENDED ABSTRACT
Introduction
The increasing scarcity of natural resources and the growing demand for food security have driven agriculture toward innovative production strategies. Among them, greenhouse cultivation has emerged as a sustainable solution, offering up to ten times higher productivity while reducing water consumption to nearly 10% compared with open-field farming. However, its higher investment cost underscores the importance of resource optimization and accurate performance prediction. In recent years, the integration of big data and machine learning (ML) techniques has transformed agricultural management by enabling precise forecasting and decision-making. Unlike traditional statistical models, ML algorithms can handle nonlinear interactions and identify hidden patterns, thereby improving prediction accuracy. In greenhouse systems, models such as artificial neural networks, support vector machines, and random forests have been successfully applied to predict climatic factors and crop yields. This study aims to develop an ML-based model to predict the performance of greenhouse crops in Iran, thereby supporting sustainable agriculture and informed policy-making.
Material and Methods
This study developed regression machine learning models to predict greenhouse crop yields (tons per hectare) across various provinces and counties, considering crop types and cultivation area. Data were collected from official agricultural yearbooks (2002–2023) including 12,426 records covering 32 provinces, 474 counties, and 33 crop types. The dataset was preprocessed with label encoding for categorical variables and normalization for numerical features using MinMaxScaler and manual scaling. Data were split into 90% training and 10% testing sets. Four regression algorithms were implemented: Support Vector Regression (SVR) with RBF kernel, Decision Tree (max_depth=128, min_samples_split=64), Multi-layer Perceptron (two hidden layers with 64 and 32 neurons), and Random Forest (200 trees, max_depth=64). Models were tuned manually for hyperparameters. Performance was evaluated using R², MSE, MAE, RMSE, and standard deviation, and statistical significance was assessed by one-way ANOVA.
Results and Discussion
After training and evaluating the models, the Random Forest (RF) model demonstrated the best performance with an R² of 0.9598 and the lowest Mean Squared Error (MSE) of 15.6×10⁶, indicating highly accurate predictions. The RF model’s complexity, involving 200 trees and a max depth of 64, resulted in a large model size (184,488 KB) but ensured robust handling of complex, nonlinear relationships in the data. The prediction plot shows excellent alignment between actual and predicted values, confirming its superior accuracy.
The Multi-layer Perceptron (MLP) model achieved the second-best performance with an R² of 0.8878 and MSE of 27.3×10⁶, outperforming SVR and Decision Tree (DT) in accuracy. Its architecture, with two hidden layers comprising 64 and 32 neurons, enabled the learning of nonlinear data patterns, although some prediction errors persisted due to the relatively shallow network depth. The model size was moderate (85 KB), reflecting the trade-off between complexity and computational efficiency. The DT model showed reasonable accuracy with an R² of 0.8864 but recorded a higher MSE of 44.3×10⁶, indicating some inaccurate predictions. Due to its simple tree-based structure, it had the smallest model size (55 KB) and the fastest computation times, making it suitable for scenarios that prioritize speed and smaller storage over absolute accuracy. The Support Vector Regression (SVR) achieved the lowest accuracy, with R² of 0.8546 and the highest MSE of 33.6×10⁶. While SVR can model nonlinear data effectively, its performance in this case was limited by its sensitivity to hyperparameter tuning. The model size was moderate (182 KB), but the prediction plots showed greater divergence from actual values compared to those of other models. Error metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and standard deviation (STD), further confirmed the RF model’s strong predictive stability with the lowest MAE and RMSE values. Statistical analysis using one-way ANOVA revealed no significant difference between RF, DT, and actual values at the 90% confidence level, while SVR showed statistically significant deviation, suggesting lower reliability. Two-way ANOVA confirmed that RF and DT had similarly high accuracy, statistically outperforming MLP and SVR.
In conclusion, RF is the most accurate and stable model for predicting greenhouse crop yield, though at the cost of higher computational demands and model size. DT offers a faster and smaller alternative with acceptable accuracy. MLP and SVR lag behind in performance, likely due to less optimal model complexity and parameter tuning. These findings suggest that RF is suitable for detailed predictive tasks and DT when computational resources are limited.
Conclusions
Greenhouses are recognized as a sustainable agricultural method, with yield performance being a key indicator of efficiency. This study evaluated four machine learning models MLP, SVR, Decision Tree (DT), and Random Forest (RF) to predict greenhouse crop yields across Iran using geographic, crop type, and cultivation area data. Among them, RF showed the best performance (R² = 0.9598, MSE = 15.6×10⁶) by effectively modeling complex relationships. DT also performed well, but it had some large prediction errors, resulting in a higher MSE. MLP and SVR exhibited weaker accuracy and significant deviations from actual values. These findings suggest that machine learning models, especially RF, can serve as powerful tools for policymakers and stakeholders in optimizing crop selection, resource management, and yield prediction. The developed RF model enables precise, location-based forecasting that can guide strategic agricultural planning and improve greenhouse productivity nationwide.
Acknowledgements
The authors sincerely extend their gratitude the Sabz Payesh Afra Watergy Systems Optimizers Company, for their kind cooperation and invaluable assistance in facilitating the collection of the necessary data for this study.
Author Contributions
Khashayar Zare: Methodology, Conceptualization, Writing, Data gathering, Data analysis
Ahmad Hosseinnejad: Methodology, Conceptualization, Supervision, Data analysis
Data Availability Statement
"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 authors received no specific funding for this research

Abstract Abstract
Nowadays, the application of vacuum infrared dryers has expanded considerably due to the high quality of the final product. This fact is noteworthy despite the challenges associated with their high energy consumption and the resulting environmental issues. The application of new approaches in the operation of this equipment can substantially contribute to addressing existing problems. In this regard, the present study introduces a novel innovative intermittent method combined with the specific application of microwave waves in a vacuum infrared dryer. The quality attributes of the final product along with energy consumption were evaluated, and ultimately the optimal operating point was proposed. For this purpose, 4-mm-thick slices of red beetroot (as a case study) were investigated during the drying process. The independent variables included temperatures of 45, 60, and 75 °C; vacuum pressures of 20, 50, and 80 kPa; applied microwave powers of 150, 300, and 450 W; and microwave application times of 0, 10, and 20 s. Response surface methodology and a central composite design were employed to analyze the obtained data and optimize the process. Physical, mechanical, bio-chemical, and energy indices were examined as dependent variables. The optimal drying conditions, determined using response surface methodology, were a temperature of 50 °C, a vacuum pressure of 20 kPa, a microwave power of 339 W, and a microwave application time of 10 s. Under these optimal conditions, antioxidant activity of 96%, vitamin C content of 6.30 mg ml⁻¹, color changes ΔL*, Δa*, and Δb* of 18.15, 5.24, and 2.62, respectively, a shrinkage percentage of 40.75%, and a specific energy consumption of 1585.45 MJ kg⁻¹ water were obtained. Achieving superior results, except for shrinkage percentage, compared with the conventional drying method, in addition to no increase in capital investment cost, indicates the success of this approach.
EXTENDED ABSTRACT
Introduction
The root part of red beetroot (Beta vulgaris) is a widely valued vegetable around the globe, recognized for its rich nutritional composition. It serves as an excellent source of essential nutrients, including dietary fiber, natural pigments such as betalains, various vitamins (A, B1, B2, B6, and C), antioxidants, and carbohydrates. The continuous growth of the world population consequences in the increasing demand for nutritious foods. Therefore, the dehydration (especially vacuum drying method) of fruits and vegetables has emerged as an essential engineering approach within the food industry. This process aims to extend product shelf life, ensure year-round market availability, minimize packaging and transportation costs, and improve the overall quality of the final product. However, drying operations are among the most energy-intensive processes in food production. Consequently, considerable research efforts have been directed toward minimizing energy consumption and reducing production costs while maintaining product quality. These efforts include the development of innovative drying technologies and the optimization of existing systems. In this study, the feasibility of a novel drying approach based on periodic temperature strategy under vacuum conditions was investigated. This tactic was considered as a potential alternative technique for developing advanced drying methods that can substantially decrease processing time and associated costs for the manufactured apparatus, while the quality attributes of the final product were improved.
Material and Methods
In this study, the performance of an infrared–vacuum dryer in normal conditions and a newly developed drying technique involving periodic temperature conditions was examined. The novel approach was further supported by microwave application. The red beetroot samples of 60×40×4 mm in dimension were exposed under periodically applied temperatures (45, 60, and 75 °C), vacuum pressure (20, 50, and 80 kPa), microwave power (150, 300, and 450 W), and microwave exposure times (0, 10, and 20 s). The study investigated the influence of drying methods on response variables, including ΔL*, Δa*, Δb*, shrinkage, vitamin C, antioxidant activity, Deff, and SEC. In the periodic drying method, a fixed temperature was maintained during each run, while microwave power was applied every 10 minutes until the samples reached the target moisture content. Experimental design, data analysis, and process optimization were carried out using the software of Design-Expert 13.0.
Results and Discussion
The lack of fit (P-value) for all studied variables in periodic technique method was found to be non-significant between 0.09 to 0.94, indicating that the developed RSM models exhibited a strong capability in predicting the variations of the response variables. The coefficient of determination (R²) values for all proposed models ranged between 95.19 and 99.70, confirming a high degree of correlation between experimental and predicted data. Furthermore, the low coefficients of variation (C.V. < 6.93) for all models demonstrate good reproducibility and reliability of the experiments. The optimal drying conditions were determined to be a temperature of 50 °C, a vacuum pressure of 20 kPa, a microwave power level of 339 W, and a microwave application time of 10 s. The selected desirability value obtained under the optimized drying conditions was 0.816, indicating a good level of optimization. The found results of vitamin C content compared to the fresh sample value of 7.53 mg/ml, indicates relatively good retention of this valuable compound. To compare the response variable values at the optimized points, an additional experiment was conducted using the vacuum dryer under normal drying conditions at 50 °C and 20 kPa, with three replications. The obtained results for vitamin C content (6.09 and 6.30 mg/ml), antioxidant activity (85% and 96%), color change parameters ΔL* (21.49 and 18.15), Δa* (6.74 and 5.24), Δb* (3.38 and 2.62), shrinkage percentage (37.57% and 40.75%), effective moisture diffusivity (2.43 × 10⁻¹⁰ and 3.43 × 10⁻¹⁰ m²/s), and specific energy consumption (2138.48 and 1585.45 MJ/kg water) were obtained for the normal and periodic drying methods, respectively. The comparative analysis of these response variables showed changes of 34.48%, 12.94%, 15.54%, 22.26%, 22.48%, 8.46%, 41.15%, and 25.86%, respectively. Except for shrinkage percentage with 8.46% value, all parameters showed improvement under the periodic drying method. This enhancement can be attributed to the synergistic advantageous effect of microwave and vacuum drying, which combines the efficient energy transfer of microwaves with the gentle drying conditions of vacuum processing. Whereas, conventional vacuum drying is relatively slow due to the absence of convective heat transfer, the incorporation of microwave energy significantly reduces the drying time while simultaneously preserving product quality.
Conclusion
The periodic drying technique in the infrared–vacuum drying process, when combined with specific microwave pre-treatment, demonstrated remarkable superiority over normal vacuum drying method. This periodic technique achieved a great improvement in effective moisture diffusivity and reduction in specific energy consumption, while providing excellent preservation of color parameters (closer to the fresh conditions) and bio–chemical quality attributes. These findings confirm the significant advantages of the periodic infrared–vacuum drying approach assisted by microwave application over the conventional vacuum drying method. The results of present study revealed a great achievement due to simultaneous enhancement in both product properties (physical, biochemical and thermal) and drying device efficiency (SEC). Among these attributes, vitamin C content is particularly significant, as its preservation generally indicates that other quality characteristics of the sample are also maintained. However, the results are specific to red beetroot, and further investigations are required to validate the applicability of this technique to other agricultural products.
Author Contributions
Nazat Hasan Jeejo: PhD student
Reza Amiri Chayjan: Supervisor
Hassan Sarikhani: Advisor
Jafar Amiri Parian: Advisor
Data Availability Statement
All data and results have been presented within the text of the article.
Ethical Considerations
The authors have adhered to ethical principles in conducting and publishing this scientific work, and this has been confirmed by all of them.
Conflict of Interest
There are no contributions of interest in this article, and this has been confirmed by all authors.

 

Abstract Abstract
The limitation of fossil resources and the environmental impacts resulting from their combustion have increased the importance of using renewable energies, including biomass energy. One of the approaches to energy production from biomass is the simultaneous generation of electricity and heat from animal manure. Given the substantial share of animal manure in biomass resources and its improper management in the country, it is essential to evaluate the potential for bioenergy production and to determine the spatial distribution of this renewable resource in different regions of Iran. Accordingly, the present study was conducted to assess the feasibility of bioenergy production from animal manure in Lorestan Province. The data required for this research were collected from relevant organizations, library resources, and the Agricultural Statistics Yearbook for the year 1402 (2023–2024). Based on the results, the total amount of collectable animal manure in the province was estimated at 355,069.7 tons, and the amount of methane produced from it was calculated to be 54.319 million cubic meters. The electrical and thermal energy and power outputs of the combined heat and power (CHP) system from animal manure were obtained as 236,287.68 MWh of electricity with a power of 33.75 MW and 265,823.62 MWh of heat with a power of 37.96 MW, respectively. The counties of Khorramabad, Borujerd, and Aligudarz ranked first to third in the province, with electricity generation potentials of 41,781.75, 38,335.68, and 32,695.04 MWh, and thermal energy generation potentials of 47,004.47, 43,127.64, and 36,781.91 MWh, respectively. In addition, the global warming potential index associated with energy production from animal manure was calculated to be 118,274.74 tons of CO₂ equivalent. According to the results, the use of electricity generated from animal manure can supply 7.11% of the total electrical energy consumption in Lorestan Province.
EXTENDED ABSTRACT
Introduction
Population growth and technological advancement mean that human activities now consume high levels of energy. This causes significant problems, including the depletion of fossil fuel resources and environmental pollution. Energy managers and planners are therefore developing options to meet these increasing demands for energy while maintaining a low environmental impact. The application of advanced energy-saving techniques, renewable energy resources, and technologies presents a viable solution to these problems. Bioenergy is a widely available resource, making it an attractive form of renewable energy. Biomass is abundant and easily accessible, making it a low-cost resource with great flexibility in terms of potential applications. One effective way to harness biomass energy is through the simultaneous production of electricity and heat from animal waste. Cogeneration, a highly efficient technology, produces both electricity and thermal energy using a variety of technologies and fuels, offering a cost-effective solution to reduce CO2 emissions. Given the significant amount of animal waste in biomass resources and its inadequate management in the country, it is crucial to assess the potential for bioenergy production and analyze the distribution of this renewable resource across different regions of Iran. This research aims to evaluate the bioenergy production potential from animal waste in Lorestan province.
Material and Methods
Data for this study were gathered from relevant organizations, library sources, and agricultural statistics for the year 2023. This section of the research focuses on the geographical location of the region, livestock and poultry data, the amount of animal waste, methods for calculating the theoretical and potential for biogas and methane production from animal waste, the examination of simultaneous electricity and heat production, and the estimation of pollutant emissions from bioenergy production using animal waste. Finally, the share of reducing the production of electric energy from fossil fuels by replacing the electric energy produced from animal waste in Lorestan province was calculated.
Results and Discussion
Based on the results, the amount of waste that can be collected from much livestock in the province was calculated to be 280318.6 tons, and heavy (cow) and light (sheep and goat) livestock with a production amount of 152594.9 and 127723.7 tons, respectively, accounted for 54.44% and 45.56% of the waste that can be collected from much livestock, respectively. Additionally, the amount of waste that can be collected from poultry units in the province was estimated to be 74,751.1 tons. Broiler and egg-laying chicken breeding units, with production amounts of 68,626.3 and 6,124.8 tons, respectively, accounted for 91.81% and 8.19% of the waste from poultry units. The annual methane production potential from livestock and poultry waste in the province was calculated to be 34.136 and 20.183 Mm3, respectively. Additionally, the total amount of animal waste (from livestock and poultry) that can be collected in the province was estimated at 355,069.7 tons, and the annual amount of methane produced from it was estimated at 54.319 Million Cubic Meters. Khorramabad, Borujerd, and Aligudarz counties were ranked first, second, and third in the province, with annual production potentials of 9.605, 8.813, and 7.516 Mm3, respectively.
Additionally, the energy, electrical, and thermal power of the CHP (combined heat and power) system derived from animal waste were calculated as 236,287.68 MWh of electricity with a power of 33.75 MW and 265,823.62 MWh of heat with a power of 37.96 MW, respectively. Khorramabad, Borujerd, and Aligudarz counties were ranked first, second, and third in the province, with the potential to produce 41781.75, 38335.68, and 32695.04 MWh of electricity, and 47004.47, 43,127.64, and 36,781.91 MWh of heat, respectively. The amount of greenhouse gas emissions from the production of electrical energy from animal waste was 118,148.45 tons/year, and the share of CO2, N2O and CH4 in the emissions was 118,143.84, 0.1181, and 4.49 tons/year, respectively. Additionally, the global warming potential (GWP) index for energy production from animal waste was calculated to be 118,274.74 tons of CO2 equivalent. Compared to gas-fired power plants, generating electricity from animal waste reduces greenhouse gas emissions by 64,309.01 tons of CO2 equivalent/year (35.2%).
Conclusions
The current significant dependence of the energy sector on fossil fuel resources has led to numerous problems worldwide, including energy security issues and environmental pollution. Many abandoned renewable resources could be utilized to generate energy and balance the energy consumption profile in this sector. One of the energy sectors that significantly relies on fossil fuels is the electricity generation sector. In addition, one of the most overlooked renewable sources of biomass originates from the agriculture sector, i.e., animal waste. Hence, the present study aimed to assess the potential for bioenergy production from animal waste in Lorestan province. Based on the results, the use of electrical energy produced from animal waste can supply 7.11% of the electrical energy consumed in Lorestan province. Therefore, by producing bioenergy from animal waste, we can take steps to reduce dependence on fossil resources and greenhouse gas emissions, and thus achieve sustainable development.
Acknowledgements
"Not applicable"
Author Contributions
Movahed Sepahvand: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing-original draft preparation, Writing-review and editing, Supervision, Project administration.
Hadis Nematpour Malek Abad: Conceptualization, Methodology, Formal analysis, Resources, Writing-original draft preparation.
Mohammad Ali Ghahremani Aghbolagh Rostam Khan: Methodology, Formal analysis, Resources.
Data Availability Statement
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.
Ethical Considerations
The authors avoided 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
Plastic wastes can cause unexpected soil contamination, reduce the quality of agricultural lands, and disrupt farming operations. The primary objective of this study is to determine reliable thresholds and compare different indices for detecting plastic coverings in fields using remote sensing technologies, aiming to manage the extent of plastic cultivation and control the volume of generated waste. In this research, the Google Earth Engine (GEE) platform and Sentinel-2 satellite imagery were employed to detect plastic covers in cucumber and watermelon fields in Hamadan Province. For this purpose, detection thresholds for plastic coverage were estimated based on the interquartile range of vegetation remote sensing indices, including NDVI and NDWI, as well as spectral plastic detection indices, including PMI, PMFI1, and PMFI2, during the growth season of sampled fields. Subsequently, the indices were evaluated according to their accuracy in detecting plastic areas based on the selected thresholds at different months of the growth season. The results indicated that the NDWI index exhibited high performance at the beginning of the growth season for both crops. However, spectral indices demonstrated variable performance in mid-season plastic detection depending on the crop type; specifically, PMFI1 showed higher accuracy in watermelon fields, while PMI was more accurate in cucumber fields for detecting plastic coverage in Hamadan Province. The findings of this study, highlighting the high accuracy of remote sensing indices in identifying plastic-covered fields, can significantly aid in decision-making, addressing environmental challenges, and managing plastic waste.

EXTENDED ABSTRACT
Introduction
Plastic mulch is widely used in the cultivation of agricultural crops, especially summer crops, due to its potential to increase crop production and optimize water consumption. Plastic mulch protects agricultural products from adverse conditions such as cold, heat, heavy rain, wind, harmful insects, and diseases. Plastic mulch is mainly made of polyethylene, and due to their polymer-hydrocarbon nature, they are very difficult to decompose under natural conditions completely. However, plastic waste can cause soil pollution, reduced quality of agricultural fields, and decreased yield. Therefore, accurate and timely monitoring of the temporal and spatial distribution of plastic mulch in large areas is very important to evaluate its effects and prevent environmental problems. Conventional techniques for mapping plastic mulch, such as topographic measurements or photogrammetric techniques, are time consuming, costly, and require experienced personnel. Remote sensing is considered the only practical method for collecting this information on a large scale and an essential tool for managing the area under plastic mulch. High resolution satellite images are valuable new resources for accurate object detection and land data collection. Determine the valid threshold for detecting plastic mulches in fields using remote sensing technologies to manage the area under plastic cultivation and the possibility of estimating the amount of plastic mulch residues in the fields is the main goal of this research.
Material and Methods
In this study, 30 cucumber and watermelon fields with plastic mulch in Hamadan province were investigated. To identify the plastic mulch used for cucumber fields from mid-April to mid-July, and for watermelon fields from mid-June to mid-October Sentinel-2 satellite data from the Google Earth Engine (GEE) system were used. For this purpose, six Sentinel-2 satellite bands and 30-meter Scale mosaic images with cloud-free MOD09A1.006 Terra Surface Reflectance data were used. The bands used were Blue, Green, Red, NIR, SWIR1, SWIR2. Based on the difference in spectral characteristics between plastic mulch and other land objects (vegetation, water, bare soil, etc.), PMFI1, PMFI2, NDVI, NDWI, and PMI indices were extracted from Sentinel-2 satellite data to identify plastic mulch in the fields. After extracting PMFI1, PMFI2, NDVI, NDWI and PMI indices from Sentinel-2 satellite, the output data of GEE system was transferred to ArcGIS software. Since different indices have different reflectances of ground objects (soil, plastic mulch and vegetation cover). Therefore, according to the desired fields and crop planting date, spectral reflectances for ground objects in different months were examined and thresholds for plastic mulch range in different indices were determined. Then, after drawing boxplot of data, the interquartile range (range of first to third quartiles) of data was considered as plastic and data less and more than that were considered as areas without plastic mulch.
Results and Discussion
The results of this evaluation showed that the evaluation indices had a significant impact on the accuracy of diagnosis, and the evaluation accuracy ranged from 11% to 97.5% depending on the indices examined. The highest accuracy of plastic mulch detection for watermelon fields was 97.5% and for cucumber fields was 91.8%, which was achieved using the NDWI indice. The NDWI index was more accurate than other plastic mulch detection indices in the study area due to its higher spectral reflectance in the NIR and the Green bands. According to the spectral bands of the Sentinel 2 satellite, the NDWI index allows some of the spectral characteristics of the underlying soil and the moisture in it to be transmitted more transparently, thus it was more accurate than other plastic cover detection indices. Among the other indices examined, the NDVI generally performed better than the spectral indices PMFI1, PMFI2, and PMI. The lowest accuracy of plastic mulch detection was for the PMI index, which was 11% for watermelon fields and 24.7% for cucumber fields.
Conclusions
The purpose of this research was to identify plastic mulch in cucumber and watermelon fields in Hamadan province. For this purpose, Sentinel-2 satellite images in the GEE cloud system were used. The results of this evaluation showed that the accuracy of plastic mulch detection varied from 11% to 97.5%, with the highest accuracy related to the NDWI indice for detecting plastic mulch in watermelon fields in June. NDWI index demonstrated the highest performance at the beginning of the growing season for both crops. However, in the mid-season, the spectral indices exhibited varying performance in detecting plastic depending on crop type: PMFI1 achieved higher accuracy for watermelon fields, while PMI performed better in cucumber fields. PMI index also had higher sensitivity to the type of crop cultivated than PMFI1 index. These findings underscore the high potential of remote sensing indices for accurately identifying plastic-mulched fields, which can significantly support decision-making in addressing environmental challenges and managing agricultural plastic waste.
Acknowledgements
The authors would like to acknowledge the Bu-Ali Sina University for finantcial support
Author Contributions
Majid Yousefian Hashemabadi: Data curation, Software, Writing
Hossein Bagherpour: Conceptualization, Methodology, Reviewing, Supervision
Mehraneh Khodamoradpour: Advisor, Conceptualization, Reviewing, Software
Data Availability Statement
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
Greenhouse cultivation is a method of agricultural production that, despite advantages such as increased water-use efficiency, higher employment, greater yield per unit area, production of higher-quality crops, off-season production, and the ability to schedule planting according to market demand, is highly dependent on energy consumption due to off-season production. Previous studies conducted in the country, both before and after the implementation of targeted subsidies, indicate that the largest share of energy consumption in greenhouses is related to the heating system. Considering national development plans aimed at optimizing fuel consumption in production units, it was essential to calculate the actual fuel consumption in greenhouses across different regions of the country with varying structures, coverings, and cropping patterns, so that in the event of adjustments in energy carrier prices in the coming years, minimal adverse impact would occur in this sector. Accordingly, in a national project at the Agricultural Engineering and Technical Research Institute, the theoretical heating energy requirements for various regions of the country were calculated. The results for greenhouses in Isfahan Province indicate that, on average, approximately 13.2% of energy is consumed excessively. Regardless of the greenhouse location or crop type, fuel consumption can be reduced through effective management practices. Additionally, adopting climate-based management strategies in the province can slow the rate of energy consumption increase alongside the expansion of greenhouse cultivation.
EXTENDED ABSTRACT
Introduction
International statistics indicate that by 2050, the world's population is expected to reach approximately 10 billion people, with the majority residing in developing countries. This population growth will make the need for energy, water, food, and interaction with the environment the top priorities of humanity in the coming decades. For this reason, scientists have focused their attention on compensating for food shortages by increasing yields per unit area and using minimal production inputs (higher production efficiency).
The introduction and use of high-yielding and transgenic varieties, the application of various types of chemical fertilizers and micronutrients, the use of chemical pesticides, and the cultivation of greenhouse crops are among these solutions. Given Iran's size, it appears to be an ideal region for the advancement of greenhouse cultivation in West Asia, where robust production of greenhouse products will not only satisfy domestic demands but also significantly contribute to the export of non-oil commodities, generate foreign exchange, and enhance the agricultural sector's trade balance.
The results of reports on the study of energy consumption in various greenhouses across the country have shown that the largest share of energy consumption in Iran is related to the energy required by heating systems. In a national project at the AERI, the amount of heating energy required for different regions of the country was calculated. Finally, solutions for optimizing fuel consumption in different greenhouse structures were presented. In this article, the results related to greenhouses in Isfahan province were presented and analyzed.
Material and Methods
In this project, according to the technical specifications of greenhouses in different regions of the province with different climates and the desired cultivation pattern, the amount of heating energy required was calculated theoretically. For this purpose, a questionnaire was initially prepared, consisting of different sections including greenhouse information, structural information, and fuel consumption information was prepared. After validation, these questionnaires were completed in 25 greenhouses in Isfahan province. Then, using the technical specifications of the greenhouses, the amount of heating energy required (theoretical) was calculated and compared with the actual fuel consumption of the greenhouses visited.
Results and Discussion
The results showed that regardless of region and crop, double-layering the greenhouse cover reduced fuel consumption. It showed the annual fuel consumption of a single-layer greenhouse with a high height (height from the eaves 4) and a rose crop in cold regions would be about 38.1 liters per square meter, which would be reduced to 26.7 liters per square meter by using double-layering the greenhouse cover. Another point obtained is the relationship between fuel consumption and the type of plant selected. The lower the heating requirement of the plant inside the greenhouse, the more significant the reduction in fuel consumption. In a double-walled greenhouse with a tomato crop in temperate regions of the province, diesel fuel consumption would be approximately 26.8 liters per square meter per year. In contrast, if the plant inside the greenhouse were changed to cloves, the amount of fuel required would be reduced to 14.8 liters per square meter.
The results of calculating fuel consumption in a greenhouse with a medium-height structure were also similar, and regardless of the type of region and crop, double-layering the greenhouse resulted in reduced fuel consumption. It showed that the diesel fuel consumption of a single-layer greenhouse with a rose crop in cold regions of the province would be about 34.8 liters per square meter per year, which would be reduced to 24.4 liters per square meter by using double-layering the greenhouse cover or in a double-walled greenhouse with a tomato crop in temperate regions, the diesel fuel consumption would be about 24.5 liters per square meter per year. In contrast, if the plant inside the greenhouse were changed to cloves, the amount of fuel required would be reduced to 13.5 liters per square meter. The results of calculating the fuel consumption in a greenhouse with a short-height structure (height from the eaves 2) also followed the same trend. It showed that the annual diesel fuel consumption of a single-layer greenhouse with a rose crop in cold regions would be about 33 liters per square meter, which would be reduced to 23.1 liters per square meter by using two layers of greenhouse cover or in a double-walled greenhouse with a tomato crop in temperate regions, the annual diesel fuel consumption would be about 23.3 liters per square meter. In contrast, if the plant inside the greenhouse were changed to carnations, the amount of fuel required would be reduced to 12.8 liters per square meter.
Conclusions
The results of past research in the country, both before and after the targeting of subsidies, show that the largest share of energy consumed in greenhouses is related to the energy required by the heating system. Therefore, it was necessary to calculate the actual amount of fuel consumption in greenhouses to optimize it as much as possible, so that if the price of energy carriers is adjusted in the coming years, the least damage will be done to this sector. For this reason, in this national project, the technical specifications of greenhouses and cultivated plants were used to calculate the amount of heating energy required for different regions of the country. In this paper, the results of fuel consumption in various commercial greenhouse structures in Isfahan Province are presented. The results of greenhouses in Isfahan province indicate an average overconsumption of 13.2% in the province. Regardless of the type of greenhouse construction and crop, greenhouse fuel consumption can be reduced through effective management methods. Additionally, by leveraging the province's climate, the trend of increasing energy consumption, along with the development of greenhouses, can be slowed down.
Acknowledgements
This project was carried out with the joint support of the Ministry of Agricultural Jihad and the Fuel Consumption Optimization Company by the Greenhouse Engineering Research Department of the Agricultural Engineering and Technical Research Institute in 13 provinces of the country.
Data Availability Statement
Not applicable.
Ethical Considerations
The authors have observed ethical principles in conducting and publishing this scientific work, and this is confirmed by all of them.
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
This work was supported by the Ministry of Jihad-Agriculture.

Abstract Abstract
Given the importance of agricultural mechanization in enhancing productivity, reducing costs, and accelerating farming operations, this study was conducted to rank the development of agricultural mechanization assessment indices in major crop operations in Iran. In the first step, key agricultural mechanization indices were identified through a literature review and expert opinions. Subsequently, the indices were weighted using the fuzzy analytic hierarchy process (FAHP). To rank the provinces of the country in terms of mechanization development, the multi-criteria decision-making method TOPSIS was applied, and the resulting rankings were further visualized geographically using GIS software to display the spatial distribution of mechanization indices. Data were collected from official sources, including the Ministry of Agriculture Jihad, the Statistical Center of Iran, and specialized surveys. The results indicate significant differences in the level of mechanization among provinces, with certain indices—such as mechanization coefficient and machinery density—having a substantial impact on the final ranking. The findings of this study can assist policymakers in resource allocation and in enhancing the productivity of agricultural mechanization.
EXTENDED ABSTRACT
Introduction
Agricultural mechanization, as one of the main pillars of transformation in production systems, plays a crucial role in increasing productivity, reducing production costs, accelerating agricultural operations, enhancing the quality of agricultural products, reducing operational time, and minimizing resource waste. International studies have shown that in countries where agricultural mechanization has been properly developed, significant growth in labor productivity and reduction in product waste have been achieved. Mechanization has also played a crucial role in adapting to climate change by enhancing access to critical planting and harvesting times. In Iran, numerous studies have been conducted in the field of agricultural mechanization in recent years, but many of them have focused on a specific region or product. Considering the importance of agricultural mechanization in increasing productivity, reducing costs, and accelerating agricultural operations, this research aimed to develop evaluation indicators for agricultural mechanization in the operations of major crops in Iran.
Material and Methods
In the first step, key indicators of agricultural mechanization were identified by reviewing sources and expert opinions. Then, using the fuzzy analytic hierarchy process (FAHP), the indicators were weighted. To rank the different provinces of the country in terms of agricultural mechanization development indicators, the TOPSIS multi-criteria decision-making method was employed. Subsequently, using GIS software, the results of the TOPSIS model ranking were utilized to display the geographical distribution of the level of agricultural mechanization indicators. Data was collected from official sources such as the Ministry of Agricultural Jihad, the Statistical Center of Iran, and specialized surveys.

Results and Discussion
By identifying and weighting eight key mechanization indicators, including mechanization coefficient, machine density, labor productivity, and fuel consumption, it was found that these indicators can be significantly effective in evaluating the performance of mechanization in different provinces. Provinces such as Khuzestan, Fars, Golestan, and West Azerbaijan, as well as Kermanshah and Hamadan, ranked at the top of the rankings. In contrast, provinces like Sistan and Baluchestan, Alborz, and Hormozgan had the lowest ranks. These differences were primarily due to differences in technical infrastructure, government support, agricultural land density, and access to mechanization services. Therefore, the findings of this study indicate that agricultural mechanization in Iran has developed unevenly and unbalancedly, despite some relative progress in certain provinces. Sensitivity analysis also showed that the mechanization coefficient and machine density indices had the greatest impact on the final ranking of the provinces. Therefore, targeted investment in these two areas can effectively improve the level of mechanization in less developed regions. The results indicate a significant difference in the level of mechanization among different provinces, with some indicators, such as the mechanization coefficient and machine density (machines per unit area), having a substantial impact on determining the final rank. The findings of this study can help policymakers allocate resources more effectively and enhance the productivity of agricultural mechanization.
Conclusions
Using FAHP and TOPSIS methods, the provinces of the country were ranked in terms of the level of mechanization development. Practical suggestions for policymakers and the Ministry of Agricultural Jihad include: allocating resources and subsidies for mechanization on a regional basis and based on the results of the ranking, designing support packages for provinces with low levels of mechanization, including facilities for purchasing machines, training and support services, and developing a network of technical and repair services in underdeveloped provinces to reduce machine downtime. It is also recommended that farmers and production unions be encouraged to adopt cooperative farming models, utilizing mechanized equipment jointly, and to enhance farmers' technical knowledge and skills through advanced mechanization training courses. Conducting prospective studies using predictive models (such as regression or machine learning) to measure the development trend of mechanization in the next decade and combining MCDM methods with spatial data (GIS) to provide decision-making maps for regional planning can help assess the development of agricultural mechanization indicators in Iran. Additionally, this study's limitations include the lack of complete access to accurate data in some provinces and reliance on secondary sources. It is suggested that more precise field data be used in future studies. International comparisons should be made to model countries with high levels of mechanization and the impact of mechanization on the productivity of the entire production system (including water productivity, inputs, and crop yield) should be examined.
Acknowledgements
The authors would like to thank Shahid Chamran University of Ahvaz and Khuzestan Agriculture and Natural Resources Research and Education Center, for providing funding for this research.
Author Contributions
Nasim Monjezi: Data collection and analysis, and initial writing
Jafar Habibi Asl: Study design and text revision
Ehsan Houshyar: Providing expert opinions and reviews
Data Availability Statement
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 authors received no specific funding for this research

Abstract Abstract
he use of drones for spraying wheat fields improves chemical application efficiency by optimizing the type of nozzle, maintaining an appropriate nozzle-to-target distance, and enhancing deposition on the target. In the present study, the performance of two spraying methods—drone-based spraying and turbine sprayer—was evaluated and compared in wheat maturity monitoring. The tested drone sprayer was an EFT model equipped with a 20-liter tank, a pump with a flow rate of 4–8 L min⁻¹, and a Micronair nozzle. The Turboliner sprayer was a trailed type with a 2,000-liter tank, a diaphragm piston pump with a flow rate of 70–120 L min⁻¹, and an operating pressure of 20–40 bar. Performance factors of both sprayers were compared during and after spraying. According to the results, the effective spray width of the drone and Turboliner sprayers was 10 m and 40 m, respectively; solution volume applied per hectare was 10 L and 250 L; spraying time per hectare was 7 min and 10.9 min; field capacity was 8.58 ha h⁻¹ and 5.5 ha h⁻¹; field efficiency was 90% and 83%; crop crushing was 0% and 1.25%; volume median diameter (VMD) was 300 μm and 690 μm; number median diameter (NMD) was 190 μm and 281 μm; spray quality coefficient was 1.57 and 2.45; spraying efficiency was 100% for both; senescence percentage was 0.05% and 0%; and wheat yield was approximately equal at 9 t ha⁻¹ for both methods. Cost estimation showed that spraying with the Turboliner, when renting the equipment, was 11.6 times higher, and if purchased, 3.7 times higher than using the drone, with 80–90% of the additional cost attributed to crop damage caused by tractor and sprayer traffic in the field. Overall, considering better spray uniformity, higher field capacity, and elimination of crop damage during spraying, the use of drone sprayers is superior and recommended over Turboliner sprayers.


EXTENDED ABSTRACT
Introduction
The increasing global population has made ensuring food security a critical challenge. Given the limited availability of water, energy, and arable land, improving the efficiency of agricultural production at various stages, including pest management, is essential to meet human food demands. Drones (UAVs) have been employed in agriculture since the 1980s, yet their application in pesticide spraying has notably expanded over the last two decades due to advancements in navigation systems, artificial intelligence, and high-capacity batteries. Drone-based spraying offers numerous benefits, including reduced fuel consumption, lower noise and chemical pollution, enhanced precision, and improved safety for operators. Despite these advantages, conventional methods such as manual mechanical sprayers remain widely used in many regions. These traditional approaches present several challenges, including excessive pesticide usage, labor shortages, low uniformity of application, environmental contamination, limited coverage efficiency, high operational costs, and lower effectiveness in pest control. Consequently, adopting drones in agriculture presents a promising solution to overcome these limitations. This study aims to evaluate and compare the technical and economic efficiency of two pesticide application methods, modern (drone) and conventional (turboliner), for controlling wheat sun pest in Isfahan province. The novelty of this research lies in the simultaneous assessment of technical indicators, such as spray uniformity and field capacity, and economic factors, including operational costs and crop damage, which have received comparatively little attention in previous studies. By integrating both technical and economic perspectives, the study provides a comprehensive evaluation of drone applications in agricultural pest management.
Material and Methods
This study compared the efficiency of a spraying drone and a conventional Turboliner sprayer in controlling Sunn pest in wheat fields of Isfahan Province. Two one-hectare wheat fields were selected, with one treated using a tractor-mounted Turboliner and the other using a spraying drone, targeting both nymphs and adult Sunn pests. During and after spraying, multiple performance parameters were evaluated, including effective working width, solution consumption, spraying time, theoretical and effective field capacity, spray quality coefficient, spraying efficiency, wheat crop crushing percentage, and crop losses caused by Sunn pest. Additionally, an economic comparison of the two sprayers was conducted. The technical performance results were statistically analyzed using a t-test, and economic data were compiled into comparative tables. This integrated assessment of technical and economic indicators provides a comprehensive evaluation of modern versus conventional spraying methods for effective Sunn pest management in wheat cultivation.
Results and Discussion
The results showed that the average effective working width for the Turboliner and drone sprayers was 40 meters and 10 meters, respectively. Although the pesticide dose was identical for both sprayers (100–125 cc/ha), the total spray solution volume applied per hectare differed significantly. The Turboliner applied the highest volume at 250 liters/ha, whereas the drone used only 10 liters/ha, representing a statistically significant difference at the 1% level. The spraying time per hectare was 10.9 minutes for the Turboliner and 7 minutes for the drone. Field capacity was higher for the drone at 8.57 ha/h, compared to 5.5 ha/h for the Turboliner. Field efficiency was 90% for the drone and 83% for the Turboliner. Using standard wheeled tractors in the Turboliner method, and based on measurements of tractor wheel width and the number of passes during spraying, crop lodging was estimated at 1.25%. The average Number Median Diameter (NMD) of droplets was 190 microns for the drone and 281 microns for the Turboliner.
In comparison, the Volume Median Diameter (VMD) was 300 microns for the drone and 690 microns for the Turboliner. Accordingly, the spray quality coefficient (VMD/NMD) was 1.57 for the drone and 2.45 for the Turboliner, indicating significantly better spray quality for the drone. Spraying efficiency, calculated using the Henderson-Tilton formula, was 100% for both methods, confirming equal effectiveness in controlling the Sunn pest. Assessment of pest damage in wheat kernels revealed that using a drone resulted in an average yield of 7.7 tons/ha, with only 0.05% of the kernels damaged.
In contrast, the Turboliner yielded an average of 9 tons/ha, with no observed pest damage. Economic analysis indicated that the average cost of drone spraying per hectare was 1,674,740 Rials for purchase and 5,913,000 Rials for rental. For the Turboliner, these costs were 19,418,150 Rials (purchase) and 21,728,000 Rials (rental). Thus, Turboliner spraying was 3.7 times more expensive when purchased and 11.6 times more expensive when rented, compared to drone spraying. Approximately 80–90% of the cost difference was attributed to crop damage caused by tractor and sprayer movement in the field, as well as the resulting yield loss.
Conclusions
A comparative study of drone and Turboliner sprayers for controlling sunn pest in wheat fields in Isfahan Province revealed that the drone's field capacity and efficiency were 56% and 8% higher, respectively, than those of the Turboliner. Moreover, the water volume required for the Turboliner spray solution was 25 times greater than that needed for the drone, highlighting the operational convenience of drones, especially under challenging conditions. For the drone, the number median diameter (NMD) and volume median diameter (VMD) of droplets were 37% and 59% lower, respectively, than those of the Turboliner. In comparison, the spray quality coefficient was 36% higher, indicating more uniform coverage. The study also showed that using the Turboliner caused at least 1.25% of the field to suffer crop lodging due to tractor and sprayer traffic, leading to irreversible yield loss. Cost analysis indicated that Turboliner spraying was 11.6 times more expensive under rental conditions and 3.7 times higher under purchase conditions, with 80–90% of the additional cost attributed to yield loss due to crop damage. Overall, considering improved spray uniformity, higher field capacity, and reduced crop damage, drone sprayers are superior to Turboliners and strongly recommended for crop protection.
Author Contributions
Heidarisoltanabadi Mohsen: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – review & editing.
Saeedi Rad Mohammad Hossein: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Supervision, Validation, Visualization.
Imanmehr Abdollah: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing -original draft, Writing – review & editing.
Momeni Davood: Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – review & editing.
Beshart Nejad Mohammad Hasan: Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – review & editing.
Data Availability Statement
Data available on request from the authors.
Acknowledgments
The authors would like to express their sincere gratitude to the experts of the Organization of Agricultural Jihad in Isfahan Province, especially Ms. Engineer Eslami, for their valuable support and assistance in implementing this research.

Ethical Considerations
The authors confirm that research ethics were considered throughout the research methodology, including data definition, data collection, data analysis, data interpretation, and plagiarism prevention, to ensure the final report's accuracy.
Conflict of Interest
The authors declare no financial or personal conflicts of interest related to this paper. Funding Statement
The authors received no specific funding for this research