Journal of Researches in Mechanics of Agricultural Machinery

Abstract This article critically analyses global renewable energy policies through 2025. It focuses on eight countries: Germany, Denmark, China, the United States, India, Brazil, the UAE, and Morocco. The analysis highlights cross-country differences in renewable energy use, costs, reliability, and public attitudes. While renewable capacity has grown and costs have fallen, significant challenges remain. High-penetration renewable systems are becoming less reliable. Policy changes in the United States threaten further progress. Tariff wars have delayed billions of dollars in clean energy projects. Land-use conflicts are also intensifying globally. By 2050, demand for critical minerals is expected to rise sharply. This will exacerbate child labor, water scarcity, and geopolitical risks in the Global South. Meanwhile, the early phase-out of nuclear power in Germany and Denmark has increased fossil fuel dependence and raised consumer prices. These outcomes reveal the limitations of relying solely on renewables. Small modular reactors (SMRs) are already in use in Russia and China. They are considered a promising low-carbon addition to renewable energy systems. Commercial deployment of SMRs is expected in Canada, the United Kingdom, and other countries between 2026 and 2030. This paper argues that a rapid, cost-effective, and fair transition to net zero requires specific policy measures. These include long-term revenue certainty, responsible mineral management, biodiversity protection, nuclear integration, and community benefit-sharing. Without these measures, the energy transition will be slower, more expensive, and less equitable.

Abstract Thermal regulation of photovoltaic (PV) systems is critical for maintaining electrical efficiency and extending component lifespan. This study presents a hybrid photovoltaic/thermal (PV/T) collector incorporating nano-enhanced phase change material (nano-PCM) for simultaneous cooling and thermal energy storage. Copper tubes filled with paraffin doped with 3 wt.% zirconium oxide (ZrO₂) nanoparticles were vertically embedded beneath the PV panel in a novel energy storage configuration to enhance heat dissipation, improve thermal uniformity, and optimize latent heat storage within the collector structure. Transient numerical simulations were conducted using ANSYS Fluent to analyze both charging and discharging phases of the thermal storage system. During the charging process, the nano-PCM absorbed excess solar heat, effectively stabilizing the thermal field and maintaining tube temperatures approximately within the range of 290–293 K, while limiting the peak collector temperature to 372–389 K and preventing localized thermal hotspots. In the discharging phase, the controlled release of stored latent heat sustained tube temperatures between 310–325 K, maintained the central collector surface temperature at 340–345 K, and kept the PV panel temperature within 300–304 K, thereby ensuring stable operational thermal conditions. The proposed system introduces a modified and innovative configuration of thermal energy storage integration, enabling improved heat management, enhanced energy recovery, and prolonged system stability. The results indicate that the simultaneous implementation of nano-PCM–based storage and optimized PV/T structural arrangement provides an effective strategy for balancing heat absorption, storage, and controlled heat release. This integrated thermal management approach is particularly suitable for solar drying applications, where a continuous and stable thermal energy supply is essential for maintaining product quality and process efficiency.

Abstract Given the importance of long-term preservation of pomegranate fruits and the reduction of postharvest losses, this study investigated the effects of chilling injury during storage. Chilling injury is one of the major problems affecting pomegranate quality during cold storage, often leading to changes in texture and internal chemical composition. This study aimed to investigate the effect of chilling injury on the physicochemical properties of 'Wonderful' pomegranate fruits at two experimental levels: fresh fruits (control) and fruits stored at 0±0.5°C for two months (chilling-injured). The measured parameters included firmness (kg/cm²), weight (g), soluble solids content (SSC, °Brix), pH, and titratable acidity (TA, % citric acid). Firmness was measured using a texture analyzer (kg/cm²), SSC with a digital refractometer (°Brix), pH with a pH meter, and TA by titration (% citric acid). The collected data were statistically analyzed using SAS to compare healthy and affected samples. This study provides the first quantitative comparison of physical and chemical properties in chilling-injured whole 'Wonderful' pomegranate fruits using multivariate analysis (PCA). Results showed that healthy fruits had significantly higher firmness than chilling-injured fruits (88.76 vs. 60.89 kg/cm², P < 0.05). Weight did not differ significantly between groups (143.38 g vs. 140.43 g, P > 0.05). SSC was higher in injured fruits (20.34 vs. 17.18 °Brix, P < 0.05). pH was lower in injured fruits (2.84 vs. 3.02, P < 0.05), while TA was higher (1.01% vs. 0.821% citric acid, P < 0.05). Overall, these findings indicate that Chilling injury leads to softer texture, increased SSC, and altered acidity. These changes suggest that chilling-damaged pomegranates are unsuitable for fresh consumption but may be redirected to juice processing, where higher SSC and TA could be advantageous.

Abstract Due to the variable and non-deterministic nature of wind, accurate prediction of wind turbine power output is crucial for integrating this renewable energy source into the power grid and optimizing operational management. This study proposes a hybrid deep learning framework for accurate short-term prediction. The proposed model, by sequentially integrating a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network, can simultaneously extract and model local patterns and short-term dependencies through the CNN layers and long-term temporal dependencies through the LSTM layers from time-series data. The data used are real operational supervisory data from wind turbines in the Manjil region of Iran over a one-year period (from April 2024 to March 2025). After outlier removal, noise reduction, and normalization, a set of key variables including wind speed, rotor speed, generator speed, and temperatures of critical turbine components were selected as input features. The performance of the proposed hybrid CNN-LSTM model was compared with three baseline machine learning models (Linear Regression, Random Forest, and Gradient Boosting) based on the common evaluation metrics of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R²). The results clearly demonstrate the superiority of the hybrid model. The CNN-LSTM model, with the lowest MAE and the highest coefficient of determination (R² = 0.892), performed significantly better than the comparative models. Correlation analyses revealed that wind speed-related variables had the greatest influence on power output. Furthermore, time-series and scatter plots demonstrated a close match between the model's predictions and actual values, effectively showcasing its ability to track complex fluctuations, seasonal variations, and even transient events. This study demonstrated that hybrid deep learning methods, due to their inherent ability to model non-linear and multi-scale relationships, represent an effective solution to the challenge of wind power forecasting. Applying this model to the operational data of a wind farm site constitutes a fundamental step towards localizing and developing intelligent management solutions for renewable energy in the country.

Abstract This study aimed to develop and compare multivariate and stepwise regression models for predicting yield and energy indices in sugar beet (Beta vulgaris L.) cultivation. Field data were collected from 83 farms in Shahrekord County, Chaharmahal and Bakhtiari Province, Iran. Energy indices—including energy ratio (ER), energy productivity (EP), specific energy (SE), and net energy gain (NEG)—were calculated based on input and output energy equivalents. The results showed an average sugar beet yield of 56.11 t ha⁻¹, with corresponding ER of 40.89, EP of 2.43 kg MJ⁻¹, SE of 0.41 MJ kg⁻¹, and NEG of 919,750.28 MJ ha⁻¹. The multivariate regression model demonstrated superior predictive accuracy (full data R² = 98.8%, 5-fold Cross Validation R² ≈ 91.0%) compared to the stepwise model (full data R² = 95.7%, 5-fold Cross Validation R² ≈ 92.5%), owing to its inclusion of interaction and quadratic effects. Key interactions—including nitrogen × phosphate (positive), phosphate × micronutrients (negative), and human labor × nitrogen (positive)—highlighted the importance of balanced nutrient management. The highest energy input shares were attributed to diesel fuel and micronutrients (together accounting for >64% of total input energy), underscoring the need for improved machinery efficiency and targeted micronutrient application. For energy indices, multivariate regression achieved excellent fit (R² = 99.54% for ER and EP; R² = 97.66% for SE), with crop yield, micronutrients, diesel fuel, and nitrogen identified as the most influential variables. The optimal application ranges for maximizing yield were estimated at 80–90 kg ha⁻¹ (phosphate), 60–70 kg ha⁻¹ (micronutrients), and 60–70 kg ha⁻¹ (nitrogen). These findings provide practical insights for optimizing energy consumption, reducing environmental impacts, and enhancing sustainable sugar beet production in semi-arid agricultural systems.

Abstract Rapid and accurate detection of plant diseases, particularly in apple orchards, plays a vital role in improving productivity and minimizing economic losses. Many previous studies in this field rely on computationally intensive models that struggle to generalize effectively under real-world and cross-domain conditions. In this study, a lightweight, accurate, and environment-robust model is proposed for apple leaf disease classification. Two pre-trained architectures, ResNet18 and EfficientNet-B0, are employed as the feature extraction backbones, while their classification heads are replaced with a newly designed two-layer MLP-based head. In this design, the extracted features are first passed through a fully connected layer with 256 neurons, followed by a ReLU activation function and a Dropout layer to mitigate overfitting, and finally connected to a fully connected output layer with a neuron count corresponding to the number of disease classes. This structure not only reduces overfitting but also enables learning nonlinear decision boundaries, enhancing model performance under multi-domain scenarios. Experimental results on the 9-class AL9EE dataset demonstrate that the proposed model achieves 98% accuracy while maintaining computational efficiency, striking an optimal balance between accuracy and processing cost. Furthermore, its simplicity and high inference speed make it highly suitable for deployment on edge devices and IoT-based agricultural systems. The findings highlight that integrating deep learning architectures with re-engineered final shallow learning layers provides an effective and practical pathway toward developing intelligent and real-time plant disease detection systems for smart agriculture applications.

Abstract Introduction:

Sustainable agricultural production depends on the efficient management of energy inputs and the mitigation of environmental impacts through all stages of the production cycle. Evaluating energy and environmental indicators has become a fundamental method for assessing the sustainability of agricultural systems by identifying inefficiencies and potential areas for improvement. West Azerbaijan Province, located in northwestern Iran, is one of the country’s major apple-producing regions, with extensive orchard areas and a long history of horticultural cultivation. Despite its economic importance, there has been no comprehensive study addressing the energy consumption patterns and environmental emissions associated with both apple production and post-harvest storage. Apple represents a cornerstone of the local agricultural economy, providing income and employment for thousands of farmers. However, the sector’s growing dependence on fossil fuels and the intensive use of synthetic fertilizers and pesticides have raised serious environmental problems. These practices contribute to soil degradation, water contamination, and greenhouse gas emissions, ultimately threatening the long-term sustainability of apple farming. Therefore, understanding the energy flow, identifying hotspots of environmental impact, and quantifying emissions across the production and storage stages are needed. Such concepts can guide policymakers and producers toward implementing energy-efficient technologies, renewable energy integration, and eco-friendly management strategies to enhance sustainability in the apple production sector.



Material and Methods:

The research was conducted in the apple orchards of West Azerbaijan Province, located at an elevation of 1,332 meters above sea level. Data were collected from a sample of literate farmers and cold storage operators through 50 questionnaires and field observations. The collected data included information related to fuel, fertilizers, pesticides, irrigation water, machinery, labour, and storage operations. The total energy inputs were calculated using standard energy coefficients. Environmental emissions associated with fertilizer and pesticide use were quantified using accepted emission factors. The life cycle assessment (LCA) was performed using the CML methodology in SimaPro 9.5, with the Ecoinvent v3.5 database. The system boundary encompassed the processes from apple cultivation in the orchard to storage. The study focused on environmental impact categories, including global warming potential, acidification, eutrophication, human toxicity, freshwater and marine aquatic eco-toxicity, terrestrial eco-toxicity, and photochemical oxidation. Energy indices such as energy use efficiency, energy productivity, and net energy were evaluated.



Results:

The findings revealed that the total average input energy for apple orchards was 116,505 MJ per hectare, with an average yield of 34,951 kilograms per hectare. In post-harvest operations, the total average input energy was 15,727 MJ per ton of stored apples. Among the inputs, fuel accounted for the largest share of total energy consumption (39%), followed by NPK fertilizers (22%) and pesticides (14%). The energy analysis showed an energy ratio of 0.72 kg MJ⁻¹, energy efficiency of 0.32 kg MJ⁻¹, specific energy of 3.33 MJ kg⁻¹, and a negative net energy value of −32,621 MJ ha⁻¹, indicating significant energy loss in the system. These results emphasize that fuel and fertilizer consumption are dominant factors influencing the overall energy balance in apple production. In terms of environmental impacts, the study showed that eutrophication potential and freshwater aquatic toxicity had the highest values, at 473 × 10⁻⁹ and 137 × 10⁻⁹, respectively. In the post-harvest stage, marine aquatic toxicity (0.0802 × 10⁻⁹) and acidification potential (0.0121 × 10⁻⁹) were the most critical categories. The high eutrophication potential shows the excessive leaching of nitrates and phosphates from fertilizers into surface and ground water, leading to oxygen depletion and threats to aquatic life. Similarly, pesticide use contributed to the categories of marine and terrestrial ecotoxicity due to its adverse effects on soil and water ecosystems. The findings indicated that fuel consumption and fertilizer use were the main contributors to global warming potential and acidification. High diesel consumption by farm tractors was a major source of carbon dioxide emissions, thus exacerbating the greenhouse effect and increasing the overall environmental footprint of apple production in the region. The energy and environmental analyses suggest that apple production in West Azerbaijan is characterized by high input intensity and relatively low energy efficiency. The excessive use of chemical fertilizers and pesticides not only increases production costs but also deteriorates soil quality and ecosystem health. In contrast, efficient management of these inputs could improve both economic and environmental performance. Implementing precision agriculture techniques, optimizing machinery operations, and adopting renewable energy systems such as solar panels for cold storage facilities can play a role in reducing non-renewable energy dependency. Furthermore, integrating organic fertilizers, compost, and biological pest control methods can help mitigate pollution while maintaining productivity. The adoption of these practices could transform apple production into a more sustainable and environmentally friendly system.



Conclusions:

The study concludes that direct emissions resulting from fuel, fertilizer, and pesticide consumption represent the main sources of environmental impact in apple production. Unbalanced fertilizer application not only fails to improve yields but also causes irreversible damage to soil structure and water quality. The categories of eutrophication and aquatic toxicity highlight the importance of nutrient management, while the contribution of fuel consumption to global warming underlines the need for energy optimization. The results show that energy use efficiency in the studied orchards is low, and the negative net energy value indicates that the current system is not sustainable. Therefore, strategies aimed at improving fuel efficiency, reducing chemical input dependence, and using renewable energy sources are essential. Installing rooftop solar panels on cold storage buildings, using high-efficiency machinery, and combining agricultural operations are practical measures to decrease overall energy consumption. Moreover, timely pruning, diversification of orchard crops, and the use of bio-based pesticides can enhance sustainability.