دانشگاه تبریز نشریه مکانیزاسیون کشاورزی 2383-126X 11 1 2026 03 21 Determination of Energy Consumption in Corn Production in Tehran, Alborz, and Qazvin Provinces تعیین انرژی مصرفی تولید محصول ذرت دانه‌ای در استان‌های تهران ، البرز و قزوین 1 12 21672 10.22034/jam.2026.70078.1344 FA احمد شریفی مالواجردی موسسه تحقیقات فنی و مهندسی کشاورزی- سازمان تحقیقات، آموزش و ترویج کشاورزی- کرج- ایران عادل واحدی موسسه تحقیقات فنی و مهندسی کشاورزی- سازمان تحقیقات، آموزش و ترویج کشاورزی- کرج- ایران سایه باقرالهاشمی موسسه تحقیقات فنی و مهندسی کشاورزی- سازمان تحقیقات، آموزش و ترویج کشاورزی- کرج- ایران Journal Article 2025 11 05 The optimum consumption of energy is one the sustainable production factors of agricultural crops. Enhancement of production will be sustainable when the use of energy is on the optimum level. The objective of this study was to assessment of energy consumption of three strategic crops including wheat, corn and sugar beet in Tehran, Alborz and Ghazvin provinces. Ten farmers have been interviewed face to face in this study. Fuel consumption measured for different agricultural operation. Results indicated that input energy of three provinces for corn was 71039.47 MJ ha-1. The average of output energy for corn was 131041.83 MJ ha-1. Maximum and minimum of consumed input energy in corn production was allocated to electricity with 56.78 % and labor with 0.24 % of the total input energy. Energy efficiency of corn obtained 1.84. Energy productivity of corn was determined 0.13 kg MJ-1. Net energy was calculated 60002.35 MJ ha-1 and energy intensity of those crops was determined 7.97 MJ kg-1. Introduction The agricultural sector, as the primary producer of the nation’s food supply, is not only a major consumer of energy but also an important provider of energy resources. In addition to technical analyses, economic, energy, and environmental assessments are crucial components in evaluating agricultural projects. Agricultural production requires energy derived from various sources. The cultivation of agricultural products demands substantial amounts of human, animal, chemical, and fossil energy inputs. Therefore, energy plays a critical role in the development and efficiency of the agricultural sector. Energy index analysis is a key necessity in agriculture. Through analyzing energy consumption patterns, strategies can be proposed to optimize energy use, minimize unnecessary losses, and enhance productivity and profitability. Considering the diverse energy use areas within agriculture, effective energy management can significantly improve resource efficiency. The main stages of energy management include controlling energy consumption, investing in energy-saving technologies, and maintaining and preserving energy resources. The objective of this study was to evaluate the energy consumption associated with corn production in the provinces of Tehran, Alborz, and Qazvin. Materials and Methods In this study, face-to-face interviews were conducted with 11 corn producers, and field observations were carried out to measure the amount of fuel consumption for various agricultural operations. Initially, a questionnaire was designed based on the opinions of agricultural experts from the Agricultural Jihad Organization and several leading farmers in the study area to obtain comprehensive data. The first two sections of the questionnaire contained general information about the farmer and the farm, such as total cultivated area, seed type, farming experience, land ownership, and agricultural machinery. Subsequent sections of the questionnaire covered all stages of corn production, including land preparation and tillage, planting, weeding, irrigation, pest control, fertilization, and harvesting operations. Each section gathered information on the quantity of inputs used, the methods and frequency of various agricultural activities, and data on input costs and revenues. The final part of the questionnaire included information related to additional activities such as field supervision, guarding, and other miscellaneous inputs. Moreover, data on the previous crop cultivated before corn were also collected. After designing and completing the questionnaires with farmers across the three provinces, the collected data were carefully evaluated and analyzed. During questionnaire design, special attention was given to the simplicity and clarity of questions. The questionnaire’s validity was further confirmed based on the opinions of university professors, agricultural experts, and results from a pilot test. A simple random sampling method was used, ensuring that the obtained results were reliable and generalizable to the entire population. To determine the total energy inputs used in corn production, the equivalent energy values of electricity, fuel, seed, machinery, human labor, fertilizers, and pesticides were calculated, and their shares in total energy consumption were determined. Results and Discussion The results indicated that in the studied provinces, the average total input energy for corn production was 71,039.47 MJ ha⁻¹, while the average corn yield across Tehran, Alborz, and Qazvin provinces was 8,914.41 kg ha⁻¹. The total output energy obtained from corn production in these provinces was calculated as 131,041.83 MJ ha⁻¹. In comparison, previous studies have reported an average yield of 6,167 kg ha⁻¹ for corn production. Among the input energy sources, electricity accounted for the highest share of total input energy, contributing 56.78%, while human labor energy had the lowest share with 0.24% of total input energy. Based on the calculated energy indices for corn production: Energy efficiency (energy ratio):1.84, Energy productivity:0.13 kg MJ⁻¹, Net energy:60,002.35 MJ ha⁻¹, Energy intensity:7.97 MJ kg⁻¹. Conclusion The results showed that the average total input energy for corn production in the provinces of Tehran, Alborz, and Qazvin was 71,039.47 MJ ha⁻¹, while the average total output energy was 131,041.83 MJ ha⁻¹. The highest share of input energy consumption was related to electricity, with 40,335.12 MJ ha⁻¹, accounting for 56.78%of total input energy. This was followed by nitrogen fertilizer, with 12,930.37 MJ ha⁻¹, representing 18.2%of total input energy. In contrast, human labor energy had the lowest share, amounting to 171.3 MJ ha⁻¹(i.e., 0.24%of total input energy). The total direct and indirect energy inputs in corn production for the studied provinces were 49,072.18 MJ ha⁻¹and 21,967.29 MJ ha⁻¹, respectively. Furthermore, renewable and non-renewable energy inputs were estimated at 43,066.42 MJ ha⁻¹and 27,973.05 MJ ha⁻¹, respectively. The average energy indices for corn production in Tehran, Alborz, and Qazvin provinces were calculated as follows: Energy use efficiency (energy ratio):1.84, Energy productivity:0.13 kg MJ⁻¹, Net energy:60,002.35 MJ ha⁻¹, Energy intensity:7.97 MJ kg⁻¹ Overall. these results indicate that electricity and nitrogen fertilizer are the most energy-demanding inputs in corn production, highlighting the potential for improving energy efficiency through better management of electrical and fertilizer use. مصرف بهینه انرژی یکی از عوامل تولید پایدار محصولات کشاورزی است. افزایش تولید محصولات کشاورزی زمانی پایدار خواهد بود که انرژی مورد استفاده در حد بهینه باشد. هدف از انجام این مطالعه بررسی مصرف انرژی در تولید محصول ذرت دانه‌ای در سه استان تهران، البرز و قزوین بود. داده‌ها از 11 بهره‌بردار در هر استان به صورت مصاحبه حضوری و عملیات میدانی جمع‌آوری شد. نتایج نشان داد که در استان‌های مورد مطالعه متوسط انرژی ورودی ذرت دانه‌ای برابر 47/71039 مگاژول در هکتار و متوسط انرژی خروجی ذرت دانه‌ای برابر 83/131041 مگاژول در هکتار است. در تولید ذرت دانه‌ای بیشترین و کمترین انرژی نهاده مصرفی به ترتیب به انرژی الکتریسیته با 78/56 درصد و انرژی نیروی انسانی با 24/0 درصد از کل انرژی‌های ورودی تعلق داشت. طبق محاسبات شاخص‌های مصرف انرژی در ذرت دانه‌ای، کارایی انرژی 84/1 ، بهره‌وری انرژی 13/0 کیلوگرم بر مگاژول، خالص انرژی 35/60002 مگاژول بر هکتار و شدت انرژی 97/7 مگاژول بر کیلوگرم به دست آمد.

https://jam.tabrizu.ac.ir/article_21672_f41561874a35ec6869b06eda82a2ab9b.pdf

دانشگاه تبریز نشریه مکانیزاسیون کشاورزی 2383-126X 11 1 2026 03 21 Investigating the Performance Parameters of ITM4120 and ITM399 Tractors Produced by Iran Tractor Manufacturing Company بررسی پارامترهای عملکردی تراکتورهای ITM4120 و ITM399 تولید شرکت تراکتورسازی ایران 13 30 21748 10.22034/jam.2026.69018.1341 FA خلیل پاشائی هولاسو گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران. آرش محبّی گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران. عارف مردانی گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران. مازیار فیض اله زاده گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران. محمدرضا زارع زاده شرکت تراکتور سازی ایران، تبریز، ایران Journal Article 2025 09 16 Considering the rapid advancement of technology worldwide and the emergence of new innovations in agricultural machinery and precision agriculture—especially in recent years—along with the growing emphasis on energy savings and the rising cost of fuel globally, and particularly in Iran, improving the quality of tractors has become essential (Lanças et al., 2024). As the most important piece of agricultural machinery, tractors play a critical role in modern farming, and enhancing their efficiency and performance is vital for sustainable agricultural development (de Melo et al., 2022; Hoy & Kocher, 2020; Zhu et al., 2022). The purpose of this research is to investigate the effects of installing a turbocharger system on engine fuel consumption, power, slip, and traction parameters in a turbocharged ITM4120 tractor compared to a conventional ITM399 tractor without a turbocharger. Materials and Methods The tests were conducted on the second-grade concrete runway at the Tabriz Tractor Manufacturing Company for various tractor evaluations, including traction tests, in accordance with the guidelines provided by the Standards Organization. The air temperature was 23 ± 7 ℃, and the air pressure was approximately 96.6 kPa. The weather conditions ranged from partly cloudy to clear, as per OECD standards. To conduct the tests, ITM4120 and ITM399 tractors were used (Figure 1). To measure the traction force between the two tractors, a 5-ton load cell model 5BBP , manufactured by Bongshin Korea , was used. This device has a measurement accuracy of 0.1 kg . A Sigma 5 dynamometer made in England was used to measure PTO power. This dynamometer has a maximum operating speed of 100 km/h, a maximum coupling weight of 100 kg, and a maximum axle weight of 1300 kg, with a power measurement accuracy of 0.1 hp and torque of 0.1 rpm. Additionally, a VDO-EDM1404 fuel gauge (manufactured in Germany ) with a measurement accuracy of ±1% was employed to measure fuel consumption. A stopwatch with millisecond accuracy was also used for precise timing during the tests. Furthermore, a thermometer was utilized to measure the ambient air temperature and ensure it remained within the OECD standard range , with an accuracy of ±0.1 degrees Celsius . (Figure 2). The specifications of the ITM 399 and ITM 4120 tractors are shown in Table 1. Preparation of test equipment and devices According to the recommendations of the standards organization and the manufacturer of the tractor and tires used, prior to the commencement of the tests, eight suitcase-shaped weights, each weighing 34 kg, were permanently installed at the front of the tractor. Additionally, two cast-iron weights, each weighing 50 kg, were permanently installed on each of the rear wheels of the tractors. ballast, in this study refers to being the tires are filled with water or left empty. Tire ballast was applied to each tractor with an air pressure ranging from 0.8 to 1 bar. To increase tractive force while maintaining the center of gravity and ensuring appropriate weight distribution for four-wheel-drive tractors, the tires were filled with water, as outlined in Table 2. The traction test was conducted on four-wheel-drive tractors in this project. The test was performed in accordance with OECD standards, in light and heavy gears, as well as tortoise and rabbit modes, at varying engine speeds. The tests were repeated three times on the concrete runway of the Tractor Manufacturing Company. During these tests, parameters such as slip percentage, tractive force, power, fuel consumption, specific fuel consumption, and specific power were measured and calculated. These values were also computed with ballast applied at maximum power across different gears (gears one, two, and three in both rabbit and turtle modes, as well as in two modes—light and heavy—using a 12-gear synchronized lever). The tests were carried out under varying loads, corresponding to 25%, 50%, 75%, 85%, and 100% of the pulling force at maximum power, as well as 50% of the pulling force in the first lighter gear, where the engine speed drops in both tractors. The data values were recorded in accordance with the standard table for all three repetitions for each tractor. Subsequently, the necessary analyses were performed on the collected data. Drawbar tensile test To conduct the tests, each of the tractors under examination (the turbocharged ITM4120 and the non-turbocharged ITM399) was hitched to the ITM1500 tractor, which was equipped with a throttle to generate drawbar pull as the load tractor. The tested tractors pulled the load tractor in different gears, while a load cell placed between the two tractors recorded the traction force. This data was logged by a data logger installed inside the cabin (Figure 3). These tests were performed for both tractors in three experimental stages. The wheel slip of the driving wheels of the test tractor, forward speed, and data from the load cell and fuel gauge were recorded in two conditions: with and without load, and with and without ballast. Results and Discussion In the results, the charts and tables related to the statistical analysis of data from the ITM4120 turbocharged tractor and the ITM399 non-turbocharged tractor's drawbar pull tests with ballast are presented. The effects of the turbocharger system on parameters such as drawbar pull force, fuel consumption, specific fuel consumption, and tractor power have been included. The statistical design employed utilized two-way ANOVA in the SAS software due to the presence of two independent variables (gear effect and tractor type). Tractors, as the primary source of mechanical power in agriculture, play a crucial role in performing agricultural operations and mechanizing crop production. As the largest tractor manufacturer in Iran, Tabriz Tractor Manufacturing Company has introduced a new tractor model equipped with an ITM4120 turbocharged engine. This initiative aligns with current global standards, with the aim of adopting modern technology and competing effectively in the international market. This study evaluated the impact of the turbocharger on the performance of ITM4120 turbocharged tractors and ITM399 non-turbocharged tractors using a ballasted material pulling test. The objective was to assess and compare the performance of the tractors in accordance with OECD standards. The test was conducted on the concrete track located at the Tractor Manufacturing Company's airport band. Statistical analysis of various parameters revealed that the ITM4120 turbocharged tractor exhibited significantly higher power output and pulling force compared to the ITM399 non-turbocharged tractor. The difference was statistically significant across all gears and speeds, with a confidence level of one percent. Additionally, the ITM4120 turbocharged tractor had lower specific fuel consumption compared and slipage to the ITM399 non-turbocharged tractor. This difference was also statistically significant at the one percent confidence level, leading to substantial fuel savings during operation.

https://jam.tabrizu.ac.ir/article_21748_a7fe75927ebc374fd15da090a438b43d.pdf

دانشگاه تبریز نشریه مکانیزاسیون کشاورزی 2383-126X 11 1 2026 03 21 Terrain Classification Using Vision Transformer for Autonomous Off-road Vehicle Navigation Systems طبقه‌بندی انواع زمین با استفاده از ترنسفورمر بینایی برای سامانه‌های پیمایش خودکار وسایل نقلیه خارج از جاده 31 42 21906 10.22034/jam.2026.71800.1355 FA بهزاد گلعنبری گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران عارف مردانی گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران Journal Article 2026 03 16 The use of autonomous vehicles in off-road environments is growing. The primary challenge of these systems is to navigate diverse and unpredictable terrain, which significantly impacts vehicle performance. This research develops a terrain classification system for off-road autonomous vehicles using the Vision Transformer (ViT) architecture. Considering the challenges of unstructured environments such as texture diversity, lighting variations, and surface roughness, the proposed model is designed based on ViT-base-patch16-224 and trained on a dataset consisting of 1757 images of 6 terrain classes (soil, rocky, grassy, muddy, gravelly, and hard). The results show that the model achieves an overall accuracy of 97% on the test data and an average F1-score of 0.90. The analysis of the confusion matrix and ROC curves indicates the model's high ability in class discrimination. However, challenges were observed in recognizing the soil class (25% error) due to the visual similarity with muddy terrain. Comparing class-by-class metrics, the model performs better in grass (F1=0.96) and gravel (F1=0.94) than in mud (F1=0.82). This study demonstrates that the ViT attention-based architecture, despite data limitations, possesses a high capability in extracting high-level features from complex images and can be utilized as part of the perceptual system of autonomous vehicles in unstructured environments. The findings suggest that adjusting class-by-class thresholds and increasing training data can improve the model's performance, particularly for complex classes. Introduction The application of off-road autonomous vehicles is rapidly expanding across diverse sectors such as agriculture, mining, military operations, forestry, environmental monitoring, and planetary exploration. Unlike urban autonomous vehicles that operate within structured environments defined by clear road markings and infrastructure, off-road vehicles must navigate unpredictable and unstructured terrains. The operational efficiency and safety of these vehicles are heavily dependent on their ability to perceive and interpret the ground they traverse. Consequently, developing robust terrain classification systems is essential for identifying traversable paths and potential hazards. While technical literature often uses terms like terrain awareness, scene understanding, and traversability estimation, the core objective remains the same: utilizing sensory data—such as images, LiDAR point clouds, and radar signals—to interpret the surrounding environment. Historically, Convolutional Neural Networks (CNNs) have dominated machine vision tasks; however, the introduction of the Vision Transformer (ViT) has marked a paradigm shift in image processing. By leveraging self-attention mechanisms, ViTs can capture long-range dependencies and complex spatial relationships within images, a capability that is critical for analyzing intricate off-road environments. A significant challenge in deploying these models, however, is their substantial demand for training data. This research aims to evaluate the efficacy of the ViT architecture in a constrained data environment, specifically without employing data augmentation techniques, to classify various off-road terrains. Materials and Methods In this study, a terrain classification system was developed based on the ViT-base-patch16-224 architecture. The dataset utilized comprised 1,757 images curated from the Roboflow platform, categorized into six primary classes: Soil, Rocky terrain, Grass, Muddy terrain, Gravel terrain, and Rough terrain. The data preparation process involved removing irrelevant images, manual verification of labels, and balancing the number of samples across classes to prevent bias. To rigorously assess the model's generalizability, a completely independent and unseen test dataset consisting of 360 images (60 images per class) was established, ensuring that none of these images were involved in the training or validation phases. Image preprocessing was executed using the ViTImageProcessor module, which included resizing images to 224×224 pixels, normalization, and conversion into PyTorch tensors. The base ViT model, featuring 12 transformer layers and 12 attention heads, was initialized with weights pre-trained on the ImageNet-21k dataset, and the final layer was modified to accommodate the six target classes. Training was conducted in the PyTorch environment using an NVIDIA 1660-Ti GPU. The training parameters were set to 30 epochs, a batch size of 32, a learning rate of 0.0001, and the AdamW optimizer. Cross-entropy loss was employed as the evaluation metric. Results and Discussion Experimental results demonstrated that the proposed model achieved a remarkable overall accuracy of 97% on the independent test dataset, despite the limited data volume. The weighted average F1-score was recorded at 0.90, indicating satisfactory model performance. Analysis of the training curves revealed that the model converged rapidly, with the loss value dropping below 0.01 after just a few epochs, suggesting an absence of overfitting. However, minor fluctuations in validation accuracy were observed, attributed to the model's sensitivity to the specific composition of data batches given the limited dataset size. A detailed class-by-class performance analysis revealed that the model excelled in identifying Grass (F1=0.96) and Gravel (F1=0.94) terrains. This success is likely due to the distinct visual features characterizing these classes. Conversely, the most significant challenge was observed in distinguishing between the "Soil" and "Muddy" classes. The confusion matrix explicitly highlighted that 25% of Soil samples were misclassified as Muddy. Qualitative analysis indicated that visual similarities in color and texture under specific lighting conditions were the primary cause of this systematic error. It appears the model relied heavily on low-level features and failed to extract higher-level attributes such as moisture content or subtle 3D structural differences. The Receiver Operating Characteristic (ROC) curve, with Area Under the Curve (AUC) values close to 1 (ranging from 0.99 to 1.00), confirmed that the model possesses high discriminative power, suggesting that the observed errors could potentially be mitigated through optimized classification thresholding. Conclusion This study confirms that the Vision Transformer (ViT) architecture holds significant potential for feature extraction from complex terrain imagery and can classify various surface types with high accuracy. However, visual similarities between specific classes (e.g., soil and mud) present a challenge that requires targeted solutions. To address these limitations, it is recommended to employ targeted data augmentation techniques (such as moisture simulation and artificial lighting), utilize hybrid architectures (combining CNNs and ViTs) for finer feature extraction, and implement class-specific threshold tuning. Furthermore, integrating multi-modal data, such as thermal or depth imagery, could enhance the distinction between visually similar surfaces. This research provides a framework for debugging deep learning model performance in unstructured environments and paves the way for developing more reliable perception systems for autonomous off-road vehicles. این پژوهش به توسعه یک سیستم طبقه‌بندی زمین برای وسایل نقلیه خودران خارج از جاده با استفاده از معماری ترنسفورمر بینایی (ViT) می‌پردازد. مدل پیشنهادی بر پایه ViT-base-patch16-224 طراحی و روی مجموعه‌داده‌ای متشکل از ۱۷۵۷ تصویر از ۶ کلاس زمین آموزش داده شد. نتایج نشان می‌دهد مدل با وجود محدودیت در حجم داده و بدون استفاده از تکنیک‌های افزایش داده، به دقت کلی ۹۷% و میانگین F1-Score معادل 90/0 دست یافته است. با این حال، تحلیل ماتریس آشفتگی یک چالش اصلی را آشکار کرد: شباهت بصری بین زمین خاکی و گلی منجر به خطای طبقه‌بندی ۲۵% در کلاس خاکی شد. این یافته نشان‌دهنده‌ی محدودیت مدل در تمایز کلاس‌های با ویژگی‌های ظاهری مشابه است. اگرچه مدل در شناسایی زمین چمنی (96/0=F1) و سنگریزه‌ای (94/0=F1) عملکرد بهتری داشت، اما نتایج به وضوح نشان می‌دهد که تنظیم آستانه‌های کلاس‌به‌کلاس و به‌ویژه افزایش داده‌های آموزشی برای کلاس‌های مشکل‌دار، راهکار کلیدی برای بهبود بیشتر عملکرد است. این مطالعه قابلیت بالای ViT را در استخراج ویژگی از تصاویر پیچیده تأیید می‌کند، اما بر ضرورت استفاده از راهکارهای هدفمند برای غلبه بر محدودیت‌های داده در محیط‌های بدون ساختار تأکید می‌ورزد.

https://jam.tabrizu.ac.ir/article_21906_3490a0e3e3e3d738408f636a1382be5d.pdf

دانشگاه تبریز نشریه مکانیزاسیون کشاورزی 2383-126X 11 1 2026 03 21 An Overview of Wind Turbine Power Plants: Costs and Revenues, Optimal Layout, and Environmental Impacts مروری بر نیروگاه توربین بادی: هزینه ها و درآمدها، آرایش بهینه و اثرات زیست محیطی 43 54 21749 10.22034/jam.2026.71662.1352 FA اباصلت بذرافشان گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران حمیدرضا قاسم زاده گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران آرمان جلالی گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران Journal Article 2026 02 24 The growing trend of the world population has brought about an inevitable increase in energy demand, and this issue, apart from the fact that non-renewable energy sources are finite, can bring about many environmental problems. Considering the importance of environmental impacts and the development of renewable energies, the construction of wind farms to absorb wind energy as one of the renewable energies is increasing all over the world. Wind is one of the important sources of energy that is clean, cheap, available and permanent. This article reviews wind power plants from their emergence and development and the research conducted in this field. One of the important factors is the economic analysis of the construction of a wind power plant, which is done by examining the investment and annual costs and the income from the power plant. Another important point in the construction of wind farms is the optimal arrangement of the farm, including the number of turbines and their arrangement, so that maximum energy production and efficiency are achieved with the lowest cost of connection between turbines. Finally, the environmental impacts of wind power plants were addressed, including the environmental impacts of electricity generation, the effects of wind turbine noise on noise annoyance, and the impact of wind power plants on air temperature. Introduction One of the solutions to energy shortages and dependence on non-renewable resources is to increase the use of renewable energy sources. In this regard, new energies, including hydropower, solar, and wind energies, are of particular importance because they do not pollute the environment. Wind energy has increased its presence in electrical power systems around the world in recent decades. Wind power plants have expanded more than any other existing technology for utilizing renewable resources in the power system due to their high efficiency, institutionalization of the technology used to exploit wind energy, low cost of electricity generation, presence of windy areas most of the time, and ability to generate power on a large scale and cost-effectively. On the other hand, the increasing technological progress of wind turbine manufacturing, which includes reducing the costs of designing and operating wind turbines, has forced countries around the world to use this energy more and more. Today, wind power (the conversion of wind energy into a useful form of energy such as electrical energy using wind turbines, mechanical energy, for example in windmills or wind pumps, or the propulsion of boats and ships, for example in sailboats) in the world has an annual production capacity of 430 TWh of electrical energy, which is 2.5% of the world's electricity consumption. The annual production capacity of electricity can be increased by expanding wind farms. Literature review Before building a wind farm, a financial and economic assessment is essential and critical. The economic assessment should be considered from both a national perspective and a power producer perspective. In the national economic assessment, the benefits of the wind farm project are compared with the costs of an alternative thermal power plant (e.g., a gas turbine), including fuel, repair, and maintenance costs. In the power producer economic assessment, the revenues are related to the present value of the electricity sales revenue over the life of the project. One of the most important and complex issues in the construction of wind farms is the optimal number and arrangement of turbines in relation to each other in order to maximize energy production and efficiency. The arrangement and installation of turbines in the farm, given the limitations of land and capital, requires precise calculations in order to obtain the most energy from the power plant. The speed of the wind exiting the turbine decreases after passing through it and some of its energy is reduced. This phenomenon is called wake. This reduction in wind speed is a function of various factors such as distance, turbine dimensions and wind speed entering the turbine. Of course, this effect improves with distance, which is due to the general air currents in the area. Therefore, the denser the turbines in a power plant, the less wind is able to recover and as a result, the output power decreases. Environmental studies conducted on wind energy show that the turbine production and wind power plant construction stage is a significant factor in greenhouse gas emissions in wind power plants, accounting for about 73-90% of the cumulative greenhouse gas emissions, and the remaining stages, including operation and maintenance, power plant demolition, and transportation, account for about 10-90%. Wind power plants, as one of the new methods of producing renewable energy with the least environmental impact compared to other energy sources, are considered one of the sources of noise pollution that have long attracted the attention of many researchers. Studies have examined the potential impacts of wind farms on global and local weather and climate. Modeling studies agree that wind farms can significantly influence local meteorology. In some cases, these effects may be beneficial, such as nighttime warming in stable conditions that can protect crops from frost. Conclusion This article aims to examine wind power plants and review the research conducted in this field. Wind is one of the clean, cheap, permanent and available renewable energy sources in the world. Many countries, including the United States and China, have made investments in this field and are making great use of this energy. Iran also has a number of wind farms, but as a country with many windy areas, if this energy source is used, it can provide a large part of its energy needs. There are important points to consider when constructing a wind power plant. The most important point is the economic analysis of constructing a wind power plant. At this stage, the investment and annual costs of the power plant are examined, and on the other hand, the income from the power plant, which includes the sale of electricity, is predicted. If it is economically viable, the wind power plant can be constructed. Another important point is the layout of the farm, which should consider the layout and number of turbines to produce the most energy for the least cost to connect the turbines. The next important point is the environmental impacts of wind power plants, both during the construction and operation stages, which should be minimized. These factors include the environmental impacts of electricity generation, the effects of wind turbine noise on noise nuisance, and the effects of the power plant on air temperature. روند رو به رشد جمعیت جهان، افزایش اجتناب ناپذیر تقاضای انرژی را به همراه داشته و این امر به غیر از پایانپذیر بودن منابع انرژی تجدیدناپذیر میتواند مشکلات فراوان زیست محیطی را به همراه داشته باشد. با توجه به اهمیت اثرات زیست محیطی و توسعه انرژیهای تجدیدپذیر، احداث مزارع بادی برای جذب انرژی باد به عنوان یکی از انرژیهای تجدیدپذیر در سراسر دنیا در حال افزایش است. باد یکی از منابع مهم انرژی است که پاک، ارزان، دردسترس و همیشگی است. در این مقاله مروری بر نیروگاههای بادی از پیدایش و توسعه آنها و پژوهش های انجام شده در این زمینه شد. یکی از عوامل مهم، تجزیه و تحلیل اقتصادی احداث نیروگاه بادی میباشد که با بررسی هزینههای سرمایهگذاری و سالیانه و درآمد حاصل از نیروگاه این تجزیه و تحلیل انجام میشود. نکته مهم دیگر در احداث مزارع بادی، آرایش بهینه مزرعه اعم از تعداد توربینها و چیدمان آنها میباشد بهطوریکه حداکثر تولید و بهرهوری انرژی با کمترین هزینه اتصال بین توربینها انجام شود. در نهایت به اثرات زیستمحیطی نیروگاههای بادی پرداخته شد که شامل اثرات زیستمحیطی تولید الکتریسیته، اثرات صدای توربین بادی بر آزردگی صوتی و تاثیر نیروگاه بادی روی دمای هوا بود.

https://jam.tabrizu.ac.ir/article_21749_b6fb022710780b58e9e7d402bb9f8e33.pdf

دانشگاه تبریز نشریه مکانیزاسیون کشاورزی 2383-126X 11 1 2026 03 21 Evaluation of Quality Changes in Honey Powder During the Spray Drying Process ارزیابی تغییرات ویژگی‌های کیفی پودر عسل در فرآیند خشک کردن پاششی 55 70 21909 10.22034/jam.2026.70751.1349 FA آرش جبلی مقدم گروه مهندسی مکانیک بیوسیستم دانشکده کشاورزی دانشگاه ارومیه - ارومیه - ایران. علی حسن پور گروه مهندسی مکانیک بیوسیستم دانشکده کشاورزی دانشگاه ارومیه - ارومیه - ایران. 0009-0001-5623-9016 فاروق شریفیان گروه مهندسی مکانیک بیوسیستم دانشکده کشاورزی دانشگاه ارومیه - ارومیه - ایران. عادل حسین پور گروه مهندسی مکانیک بیوسیستم دانشکده کشاورزی دانشگاه ارومیه - ارومیه - ایران. احمد پیری گروه مهندسی مکانیک بیوسیستم دانشکده کشاورزی دانشگاه ارومیه - ارومیه - ایران. Journal Article 2025 12 17 The high viscosity and inherent stickiness of honey pose substantial challenges for handling, transport, packaging, and its incorporation as a food ingredient. Converting honey into a powder can mitigate many of these constraints and broaden its practical applications. This study aimed to produce honey powder via spray drying and to characterize the physicochemical quality of the resulting product. Spray drying was conducted at three inlet air temperatures (120, 150, and 180 °C), three atomization pressures (0.5, 1.0, and 1.5 bar), and three maltodextrin carrier levels (15, 32.5, and 50% w/w, based on honey dry matter). Process conditions were optimized using response surface methodology, with particular emphasis on atomization pressure as a key operating variable. The powder was evaluated for moisture content, water activity, bulk density, flowability, color parameters, pH, sucrose content, fructose-to-glucose ratio, and hydroxymethylfurfural (HMF) concentration. Analysis of variance indicated that inlet air temperature and maltodextrin concentration significantly affected (p < 0.05) most quality attributes. Temperature influenced 9 of the 11 measured responses and was the dominant factor overall, with pronounced effects on moisture, water activity, bulk density, flowability, HMF formation, and color indices. Maltodextrin concentration significantly affected water activity, pH, sucrose content, fructose-to-glucose ratio, and yellowness. Atomization pressure also contributed significantly to several critical responses, including moisture content, bulk density, water activity, yellowness, and HMF concentration. Multi-response optimization identified optimal conditions at an inlet air temperature of 170–180 °C, atomization pressure of 1.0–1.5 bar, and maltodextrin concentration of 45–50%. Under these settings, the process yielded honey powder with moisture content below 2%, water activity below 0.30, high flowability, desirable lightness, and low HMF levels. Introduction Honey is a highly hygroscopic and sticky material, making its storage, handling, and industrial application difficult. Spray drying is an effective technique for converting liquid honey into powder, improving shelf life, flowability, and product stability. However, the high sugar content and low glass transition temperature of honey often cause stickiness and wall deposition during drying. Maltodextrin is commonly used as a carrier to overcome these limitations. Although previous studies have mainly focused on inlet air temperature and carrier concentration, the influence of atomizer pressure has received limited attention. Therefore, this study investigated the combined effects of inlet temperature, maltodextrin concentration, and atomizer pressure on the physicochemical properties of spray-dried honey powder. Materials and Methods Honey (Azar Kando, Iran) was mixed with maltodextrin at 15, 32.5, and 50% (w/w) and dried using a laboratory-scale spray dryer. Process optimization was performed using a Box–Behnken response surface design with three independent variables: inlet air temperature (120–180 °C), atomizer pressure (0.5–1.5 bar), and maltodextrin concentration (15–50%). Seventeen experimental runs were conducted. The responses included moisture content, water activity, bulk density, flowability, pH, sucrose, fructose/glucose (F/G) ratio, hydroxymethylfurfural (HMF), and color parameters (L*, a*, and b*). Results and Discussion Response surface models adequately described all responses, with inlet air temperature identified as the most influential processing factor. Increasing temperature and maltodextrin concentration significantly reduced moisture content and water activity, while higher atomizer pressure further enhanced drying by producing finer droplets. The lowest moisture (<2%) and water activity (<0.30) were achieved at high temperature, high carrier concentration, and high pressure. Bulk density and flowability improved with increasing temperature and maltodextrin concentration. Higher atomizer pressure also enhanced powder flowability through the formation of smaller and more uniform particles. A slight increase in pH was observed with increasing temperature and maltodextrin concentration. Sucrose content increased under higher temperature and carrier levels, indicating better sugar retention during drying. In contrast, the fructose/glucose ratio decreased with increasing temperature and pressure because fructose is more susceptible to thermal degradation, whereas higher maltodextrin concentrations protected fructose and helped preserve this ratio. HMF formation increased markedly at temperatures above 150 °C due to enhanced sugar degradation and non-enzymatic browning. Nevertheless, increasing maltodextrin concentration and atomizer pressure partially reduced HMF formation by shortening drying time and protecting sugars from excessive thermal damage. Color was also affected by processing conditions. Increasing temperature reduced lightness (L*) and increased redness (a*), reflecting stronger browning reactions. Maltodextrin improved lightness by protecting color compounds, while atomizer pressure showed only a minor influence on color compared with temperature. Unlike most previous studies, this work demonstrated that atomizer pressure is an important process variable. Although its effect was generally smaller than that of temperature, optimizing atomizer pressure significantly improved moisture removal, water activity, flowability, and HMF control, thereby enhancing overall powder quality. Conclusion The physicochemical quality of spray-dried honey powder was mainly governed by inlet air temperature, followed by maltodextrin concentration and atomizer pressure. High temperature improved drying efficiency and powder flowability but promoted HMF formation and darkening. Maltodextrin protected sugars, improved powder stability, and enhanced lightness. Most importantly, this study confirmed that atomizer pressure, a parameter rarely investigated in previous honey spray-drying studies, plays a significant role in improving drying performance and final product quality. Multi-response optimization indicated that inlet temperatures of 170–180 °C, 45–50% maltodextrin, and 1.0–1.5 bar atomizer pressure provided the best balance between low moisture, low water activity, high flowability, acceptable HMF levels, and desirable color characteristics. These findings provide practical guidance for industrial production of high-quality honey powder and emphasize the importance of incorporating atomizer pressure into future spray-drying optimization studies. ویسکوزیته بالای عسل و ماهیت چسبنده آن، حملونقل، بستهبندی و کاربردش در صنعت غذا به عنوان افزودنی را با مشکل روبهرو میکند. برخی از این مشکلات با عرضه محصول به شکل پودر قابل حل است. هدف از این پژوهش، تولید پودر عسل در خشککنپاششی و بررسی ویژگیهای کیفی محصول نهایی پودر میباشد. در این پژوهش پودر عسل با دستگاه خشککنپاششی در سه سطح دمای هوای 120، 150 و180درجه سلسیوس، سه سطح فشار اتمایزر 5/0، 1 و 5/1 بار و نسبتهای 15، 5/32 و 50 درصد کمکخشککن مالتودکسترین نسبت به جرم ماده خشک عسل به روش سطح پاسخ مطالعه شد. در تحقیقات قبلی دما و درصد حامل بررسی و نقش فشاراتمایزر در تولید پودر عسل کمتر مورد مطالعه بوده است. فاکتورهای فیزیکوشیمیایی مورد ارزیابی شامل محتوایرطوبت، فعالیتآبی، چگالی، جریانپذیری، رنگ و pH، درصدساکارز، نسبت فروکتوز به گلوکز و HMF پودر مورد بررسی و آزمایش قرار گرفتند. نتایج آنالیز واریانس نشان داد که در اغلب شاخصها، اثر دما و درصد مالتودکسترین در سطح 5 درصد معنیدار بوده و بر کیفیت نهایی پودر عسل تأثیر مستقیم دارد. بر این اساس در ۹ ویژگی از ۱۱ ویژگی، دما اثر معنی‌دار داشته و غالباً مهم‌ترین عامل تغییرات است. دما به ویژه بر رطوبت، فعالیتآبی، چگالی، جریان‌پذیری،HMF و شاخص‌های رنگی بیشترین تأثیر را نشان داد. درصد مالتودکسترین در بیشتر ویژگی‌ها به‌خصوص فعالیتآبی، pH، ساکارز، نسبت فروکتوز به گلوکز و گرایش به زردی اثر معنی‌دار داشت. فشار در چند ویژگی مهم مانند محتوایرطوبت، چگالی، فعالیتآبی و گرایش به زردی و HMF اثر معنی‌دار داشت. نتایج این تحقیق نشان داد که بهترین شرایط این فرآیند در دمای ورودی 170–180 سلسیوس، فشار 1 تا 5/1 بار و درصد حامل 45–50 حاصل شد. در این شرایط، پودری با رطوبت زیر 2 درصد، فعالیتآبی کمتر از 3/0، جریان‌پذیری بالا، روشنایی مطلوب و مقدار HMF پایین به‌دست آمد.

https://jam.tabrizu.ac.ir/article_21909_e161769d1a29611f3410788a63fe5812.pdf

دانشگاه تبریز نشریه مکانیزاسیون کشاورزی 2383-126X 11 1 2026 03 21 Intelligent Health Monitoring of Dairy Cattle Using Artificial Intelligence-Based Signal Processing: An Integrated Approach in Precision Agriculture and Internet of Things (IoT) Systems پایش هوشمند سلامت گاو شیری با پردازش سیگنال مبتنی بر هوش مصنوعی: راهکاری یکپارچه در کشاورزی دقیق و سیستم‌های اینترنت اشیاء (IoT) 71 84 21750 10.22034/jam.2026.68349.1338 FA حسین نوید گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران. هادی رحمن زاده بهرام گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران. علی حسینخانی گروه مهندسی تغذیه دام، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران. Journal Article 2025 09 01 Precision livestock farming has emerged as an effective approach for improving animal health, welfare, and production efficiency through continuous behavioral monitoring. Among the various behavioral indicators, feeding and rumination are recognized as two of the most informative physiological activities because changes in these behaviors are closely associated with metabolic disorders, digestive diseases, and overall animal health. Conventional monitoring techniques, including visual observation and camera-based systems, are labor-intensive, costly, and difficult to implement in large-scale dairy farms. Consequently, the development of low-cost intelligent monitoring systems capable of real-time behavioral analysis has become an important research objective. Introduction This study presents a comparative investigation of two signal processing approaches for dairy cattle behavior recognition using a low-power piezoelectric sensing system integrated with artificial intelligence techniques. The primary objective was to evaluate the effectiveness of conventional time-domain feature extraction and Discrete Wavelet Transform (DWT)-based feature extraction for identifying feeding and rumination behaviors while considering both classification accuracy and computational efficiency. The proposed framework combines wearable sensing, wireless data acquisition, signal preprocessing, deep learning, and Internet of Things (IoT) communication into a unified monitoring platform suitable for precision livestock farming. Materials and Methods Data acquisition was performed at the dairy farm of the University of Tabriz using a piezoelectric sensor mounted on the animal's halter to capture jaw movement vibrations during different activities. An ESP32 microcontroller was employed for signal acquisition and Bluetooth communication with a mobile application, enabling wireless data collection and storage. The recorded datasets were transferred to a cloud storage platform for subsequent processing in Python using the Google Colab environment. Signal preprocessing included digital noise filtering, Min–Max normalization, and segmentation into fixed-length windows containing 20–30 samples. Because of the imbalance between behavioral classes, particularly the limited number of feeding samples, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to generate balanced training data. Results and Discussion Two independent feature extraction strategies were investigated. In the first approach, statistical time-domain descriptors, including the mean, standard deviation, minimum, and maximum values, were extracted from each signal segment. In the second approach, Discrete Wavelet Transform (DWT) was employed to obtain time-frequency representations capable of capturing transient characteristics of jaw movements. Both feature sets were subsequently used to train identical one-dimensional Convolutional Neural Network (1D-CNN) models implemented in TensorFlow/Keras. The network architecture consisted of successive convolutional, batch normalization, activation, pooling, dropout, and fully connected layers. Model optimization was performed using the Adam optimizer with categorical cross-entropy loss, while Early Stopping and adaptive learning-rate reduction were incorporated to improve convergence and prevent overfitting. Model performance was evaluated using validation accuracy, precision, recall, F1-score, confusion matrices, and learning curves. The experimental results demonstrated clear differences between the two signal processing strategies. The time-domain approach achieved the highest overall performance, reaching a validation accuracy of 78.86%, while requiring only 21.68 minutes for model training and approximately 0.17 million trainable parameters. In contrast, the DWT-based approach achieved a validation accuracy of approximately 76%, required 23.44 minutes of training time, and increased the model complexity to approximately 0.19 million parameters. These findings indicate that eliminating wavelet transformation significantly reduces computational complexity while maintaining superior classification performance, making the proposed approach particularly suitable for resource-constrained intelligent monitoring systems. A detailed analysis of individual behavioral classes revealed different strengths for the two approaches. For the Feeding class, the DWT-based method achieved a slightly higher F1-score (0.7627) than the time-domain approach (0.752), suggesting that time-frequency decomposition can better represent the transient and irregular characteristics of feeding behavior. Conversely, the time-domain method produced superior performance for Rumination, achieving an F1-score of approximately 0.9903, compared with 0.9888 obtained by the DWT-based model. Since rumination exhibits highly repetitive and periodic jaw movements, its essential characteristics can be effectively captured using simple statistical features without requiring computationally expensive spectral analysis. The comparative evaluation indicates that the selection of signal processing techniques should be guided by the intended operational objectives of intelligent livestock monitoring systems. For large-scale dairy farms where computational resources, energy consumption, and implementation costs are important considerations, the conventional time-domain approach provides an excellent balance between classification accuracy, processing speed, and model simplicity. In contrast, DWT-based analysis may be advantageous in applications requiring enhanced sensitivity for detecting feeding behavior, despite its higher computational cost. Conclusion Overall, this study demonstrates that accurate behavioral monitoring of dairy cattle can be achieved using a low-cost piezoelectric sensing platform combined with deep learning techniques. The proposed framework offers a practical solution for IoT-enabled precision livestock farming by integrating wearable sensing, wireless communication, intelligent signal processing, and automated behavioral classification. The results further suggest that computationally efficient time-domain feature extraction represents the most suitable strategy for real-time deployment in practical livestock monitoring systems, while wavelet-based analysis remains a promising complementary technique for applications requiring more detailed characterization of complex behavioral patterns. These findings contribute to the development of intelligent animal health monitoring systems capable of supporting sustainable dairy farming through continuous, automated, and data-driven behavioral assessment. این مطالعه باهدف مقایسه دو روش پردازش سیگنال (بدون تبدیل موجک و با تبدیل موجک گسسته (DWT)) در پایش هوشمند فعالیت‌های گاو شیری انجام شد. با توجه به اهمیت کشاورزی دقیق و پایش سلامت دام، تمرکز اصلی بر تحلیل عملکرد این روش‌ها در تشخیص الگوهای حیاتی مانند تغذیه (Feeding) و نشخوار (Rumination) قرار گرفت. داده‌های مورد استفاده از حسگر پیزوالکتریک با فرکانس نمونه‌برداری ۲۴ هرتز جمع‌آوری شد و با استفاده از تکنیک‌های پیشرفته یادگیری عمیق و پردازش سیگنال تحلیل گردید. نتایج نشان داد که روش بدون DWT با دقت اعتبارسنجی 86/78٪، زمان آموزش کوتاه‌تر (68/21 دقیقه) و پیچیدگی مدل کمتر (17/0M پارامتر)، به‌عنوان گزینه بهینه برای سیستم‌های عملیاتی با محدودیت منابع محاسباتی شناخته می‌شود. با این حال، روش با DWT در کلاس Feeding بهبود جزئی در F1-Score (7627/0 نسبت به 752/0) نشان داد که حاکی از پتانسیل آن در پایش دقیق‌تر فعالیت‌های حیاتی است. در مقابل، عملکرد روش بدون DWT در تشخیص کلاس Rumination با99/0 F1-Score ≈برتری واضحی داشت. این پژوهش راهکاری کاربردی برای توسعه سامانه‌های IoT در کشاورزی هوشمند ارائه می‌کند و تأکید می‌نماید که انتخاب روش پردازش سیگنال باید مبتنی بر اولویت‌های عملیاتی (دقت، سرعت، یا تمرکز بر کلاس‌های خاص) باشد. همچنین پیشنهاد می‌شود که اثرات این روش‌ها در حوزه‌های گسترده‌تری مانند پردازش تصاویر سنجش از دور و رباتیک کشاورزی مورد بررسی قرار گیرد.

https://jam.tabrizu.ac.ir/article_21750_121d64fda112388da9b7031c1369b9c9.pdf