نشریه مکانیزاسیون کشاورزی
سال نهم شماره 3 (پاییز 1403)

Artificial intelligence (AI) plays an essential role in enhancing productivity, reducing costs, and improving service delivery across various sectors, especially in agriculture. This paper offers a comprehensive review of recent advancements in AI applications for predicting agricultural product performance. Emphasizing the potential of AI, specifically machine learning (ML) algorithms, in precision agriculture, the study highlights its impact on crop yield prediction for crops such as rice, wheat, sugarcane, and soybean. The paper explores the benefits that AI brings to farmers' decision-making by enabling accurate yield predictions, addressing the urgent need for optimized resource utilization, and meeting increasing food demands.

The study focuses on the application of various machine learning algorithms, including supervised and unsupervised learning methods, to predict agricultural product performance. Algorithms such as regression, decision trees, support vector machines, and advanced models like artificial neural networks (ANNs) were analyzed. Several tools, including TensorFlow, Keras, and Scikit-Learn, were employed for model development and testing. These tools facilitated data handling and modeling processes, enabling the extraction of significant patterns from large agricultural datasets through data mining. This approach offers insights into critical factors affecting crop yields and helps refine AI models for more accurate predictions.

The performance of multiple machine learning algorithms was assessed by evaluating their ability to predict crop yield across various crops. Artificial neural networks, random forest, and support vector machine models demonstrated the highest accuracy in predicting crop performance, making them particularly suited for applications in precision agriculture. Despite the promising results, challenges remain, such as ensuring high-quality data, improving model interpretability, and adapting algorithms to specific agricultural contexts. Addressing these challenges can enhance the models’ practical application in real-world scenarios, allowing farmers to make more informed decisions based on precise yield forecasts.

This review underscores the effectiveness of AI-based models, particularly ANNs, random forests, and support vector machines, in predicting agricultural yield with high accuracy. By addressing limitations in data quality, model interpretability, and environmental adaptation, AI models have the potential to revolutionize agriculture, enabling farmers to manage resources more effectively and make data-driven decisions to maximize crop yields. The ongoing improvement of AI tools and techniques is essential for addressing the challenges in precision agriculture and meeting the global food demands of the future.

Storage of agricultural products is one of the post-harvest operations. The increase in food waste and the emphasis on fresh consumption of these products doubles the importance of storing them after harvest. Therefore, it is very important to use methods such as a modified atmosphere, edible chitosan coating, and nano packaging film to increase shelf life and maintain the quality of agricultural products. Zucchini is one of the agricultural products that, due to having a lot of water, after a few days of storage, its quality decreases. In this research, the physicochemical and mechanical properties of this product, during the storage period were evaluated by factorial test in a completely randomized design. Zucchini product after harvesting from the farm is covered with 0.5% chitosan solution and with two types of films, including silicone nanoemulsion film and normal, and also in two environments including modified atmosphere (5% oxygen, 10% carbon dioxide, and 85 % nitrogen) and normal atmosphere, packed and stored at 4°C. The storage time for the samples was 20 days. Chemical properties (total soluble solids, pH, titratable acidity, and phenol), physical properties (color changes and weight loss), and mechanical properties (penetration force, elasticity modulus, and penetration energy) were evaluated during the storage period every four days. The results showed that the total soluble solids and color changes (∆E) in the uncoated package containing modified atmosphere and nanofilm had the least changes. The coated package containing a modified atmosphere and nanofilm reduced the changes in pH factors, titratable acidity, weight loss, and penetration force. The modified atmosphere showed a positive performance in reducing the changes of phenol. Also, the combination of modified atmosphere and nanofilm prevented the increase of changes in elasticity modulus and penetration energy.

This study has been conducted on the vital role of life cycle assessment (LCA) as a comprehensive method to evaluate the sustainability of biodiesel power generation, with a case focus on the Moghan region. Life cycle assessment covers all stages of biodiesel's life cycle, including production, distribution, and use, and provides a complete view of its environmental impacts. This study uses methods such as cradle-to-grave analysis, carbon footprint calculation, and various impact assessments to measure ecological outcomes. This article examines the distinctive features of the Moghan region's local agricultural practices, biodiesel production methods from rapeseed as the dominant oil crop in the region, and energy consumption trends. The results show that the use of fuel, plastic, and gas in rapeseed cultivation has the most intermediate effects (about 5-25% higher than other inputs) in the Moghan region. In addition, electricity consumption significantly (about 200-300% more than other inputs) affects the intermediate effects during the rapeseed oil extraction process in the Moghan region. Evaluations show that inputs of water (on average about 300, 350, 250, and 400% more than oil, sodium hydroxide, hydrochloric acid, and methanol) and electricity (on average about 150, 170, 120, and 200% more than oil, sodium hydroxide, hydrochloric acid, and methanol) can contribute the most to the environmental impacts in the biodiesel production stage from rapeseed oil in the biodiesel region. In addition, this study identifies that the biodiesel fuel itself has the greatest effect on the intermediate indicators of electricity production from canola-derived biodiesel.

Nowadays, due to the availability of large amounts of data, data analysis approaches have shown their potential to solve some problems in different economic sectors, for example, the concept of big data analysis has entered various disciplines, such as insurance, banking, agriculture, and environmental studies. Data analysis is performed using one of these three methods, i.e. regression analysis, clustering, and classification. To estimate the relationship between one or more independent variable(s) and a single dependent one, the regression analysis is used; a set of methods that allow the grouping of different agricultural objects is performed by clustering; and classification aims to categorize objects based on their properties, which are called predictors. Some categories of software tools are used for big data analysis such as image processing, machine learning, cloud-based platforms for large-scale information storing, analysis and computation, GIS systems, modeling and simulation, statistical tools, and time-series analysis. The R programming language, an open-source software, is a powerful platform to conduct big data analysis requiring machine learning processes, and statistical operations. This software also acts as a suitable tool for data visualization.

The aim of this research is the development of spatio-temporal maps of the value of agricultural crops (in five groups named cereals, legumes, industrial crops, vegetables, and fodder crops) produced in provinces of Iran using the tmap package of the RStudio software. The raw data used in this study was obtained from statistical tables presented by the Ministry of Agriculture Jihad statistics center about the value of different agronomy crops produced in 31 provinces from 2016 to 2020. Furthermore, in this study, some statistical methods were used to compare provinces based on the spatial-temporal deviations of agricultural crops produced from Iran’s official cultivation pattern. The clustering methods utilized herein were the K-means and K-medians methods of the partitioning clustering paradigm, and a hierarchical clustering method.

According to the results of this study, large deviations were recorded for Gilan, Alborz, and Yazd provinces followed by Bushehr, Tehran, and Qom provinces. Numerically, average deviations for the three leading provinces were 72.5%, 72%, and 70.95%, respectively. Furthermore, the average absolute deviations of crop yields in the cereals and vegetables categories from the official crop pattern were 5.9%, and 11.7% respectively; while similar measures for the legumes, fodder, and industrial crop categories were 30.2%, 36%, and 41.2% respectively. Moreover, clustering Iran provinces using the K-means and K-medians methods showed that by increasing the number of clusters, the results of these methods converge. Finally, from the practical vantage point, if the clustering curve contains clusters having central symmetry, by exchanging the cultivation patterns of these clusters, the yields of agronomy crops will be changed in the direction of matching the suggestions of the official cultivation pattern.

It is concluded that the existence of reliable input data of agricultural crops produced in provinces, the creation of spatial-temporal maps, and clustering provinces based on the deviations of crops produced in them from the official cultivation pattern helps main decision makers to obtain an appropriate view to establish suitable laws in compliance with matching the real production of agricultural crops with the suggestions of the cultivation pattern.

This study aimed to investigate the effect of tillage and sowing techniques on chickpea growth and yield in the Charoimak district of Eastern Azarbaijan. Maintaining crop residues on the soil surface by using conservation tillage significantly reduces weed germination. Conservation tillage improves carbon fixation in the soil and significantly prevents its release into the atmosphere.

The study was conducted in a randomized complete block design with five replications. Some parameters were measured, such as establishment percent, plant height, number of main branches, stem weight, leaf number, leaf index, leaf eight, root depth, grain weight in each plant, biomass of plants, and harvest index. At the seed maturity stage, chickpea plants were randomly harvested and the desired traits were measured. Plants from each plot were harvested from the middle rows of the plots after considering the 0.5 m margin effect.

Linear sowing with a combinate machine and conservation tillage method resulted in a 30.6 % and 39.7 % increase in seed yield compared to traditional sowing. Planting with protective tillers statistically caused a similar increase in grain yield. Whereas, among the main components of grain yield, the conservation tillage treatment increased the number of grains per plant as compared to linear sowing and increased this trait by 26.1%, while the treatment of Linear implantation increased this attribute by 14.6%. One hundred seed weight increased by only 14% compared to traditional seed sowing under linear sowing. Percentage of chickpea seed emergence only increased the effect of planting with protective tillage by 10.9% compared to traditional seed planting. Vegetative traits such as leaf weight and leaf area were increased under linear tillage and with conservation tillage, which was increased by planting with conservation tillage.

The results of this study indicate that there was no significant difference in germination percentage, number of leaves, number of main branches, and stem weight between planting with a compound tiller and traditional seed planting. However, planting with a direct-tillage row cultivator led to a significant increase in these traits. Specifically, the direct tillage treatment resulted in increases of 10.9%, 21.8%, 76.9%, and 82.9% in these traits, respectively. Notably, the direct tillage treatment had the greatest impact on stem mass. Additionally, in terms of plant height, root development depth, seed mass per plant, and seed yield per hectare, both the combined tillage and direct tillage treatments caused statistically significant and similar increases. Among these traits, the most substantial increase attributed to the direct tillage treatment was observed in leaf dry weight, which rose by 185%. The study found that the grain yield under the direct tillage treatment increased primarily due to an increase in the number of seeds. In contrast, the combined tillage treatment improved grain yield by increasing both the number of seeds and the weight of one hundred seeds. Overall, Conservation tillage methods had a higher yield than conventional systems and there was no significant difference between yields of conservative methods treatments. Concerning the reduction of costs, a direct drilling method is suggested. Of course, some important factors like machine prices and investment, the time needed to complete sowing operation in each system, and fuel consumption should be processed more.It is suggested that:- To ensure the results and generalize them, the experiments be repeated in different regions of the province.- The effect of planting methods on the physical and chemical properties of the soil affecting the growth of chickpea plants should be investigated.- The effect of planting methods on the phenological characteristics of chickpeas should be investigated.- The effect of planting methods on the biological and symbiotic characteristics of chickpeas should be investigated.

Today, farmers are striving to increase their yields, but many lack the necessary information to analyze energy consumption patterns effectively. Therefore, conducting an energy analysis is essential to provide farm planners and policymakers with a comprehensive overview of energy consumption. Energy input-output analysis is closely linked to agricultural practices, including input quantities, production levels, and environmental factors. The greenhouse industry has been active for several years, but due to a lack of awareness among those involved in the field, many producers struggle to achieve profitable outcomes. Moreover, this oversight can lead to significant environmental harm due to excessive energy consumption at the end of the production process. This research investigates the cultivation of various crop types grown in greenhouses, drawing on studies conducted by other researchers in the field.

The energy consumption involved in cultivating tomatoes was compared between field and greenhouse methods. Data was gathered through a questionnaire, which included general information about cultivation types, water resources, product inputs, and machinery used. Direct and indirect energy inputs were classified, and energy indexes were calculated based on this information. Statistical methods were employed to analyze the collected data. To estimate the energy consumed during tomato production, we first identified and measured all inputs and outputs. The energy associated with each input and output was then calculated by using the energy equivalent for each, multiplying it by the amount of input consumed or product produced.

The results indicated that total energy consumption for conventional farming systems was 81.73 GJ/ha, while for greenhouse systems it was significantly higher at 89.225 GJ/ha. In conventional systems, the primary energy input was electricity, followed by fertilizer. In contrast, for greenhouse systems, natural gas was the main energy source, with diesel as the second most utilized input. Despite the much higher energy consumption in greenhouse systems, the total income from greenhouse-grown tomatoes was three times greater than that from field-grown tomatoes. A key factor contributing to this higher income is the timing of product sales, as tomatoes from greenhouses are available in autumn, winter, and spring, periods when field tomatoes are scarce in the market.

Firstly, reducing energy consumption in this sector lowers production costs and increases profitability, while also ensuring the production of high-quality products. Secondly, by adopting new methods, we can minimize the inappropriate use of energy in production (such as fossil fuels, water, fertilizers, and pesticides), which often leads to environmental destruction. Ultimately, implementing a greenhouse production system may be a more effective approach for the region. However, it is essential to incorporate innovations such as sustainable energy systems and optimal design parameters for structures and buildings.The results of this study highlight the inefficiency of energy consumption in tomato cultivation. It was found that farmers do not properly implement the air-pile system in open-air cultivation, leading to inefficient water usage. This issue could be addressed by adopting modern irrigation methods used in the greenhouse sector. Additionally, the amount of fertilizer applied in tomato production is also inefficient. This inefficiency may stem from the inadequate use of chemical fertilizers based on soil tests.The electricity consumed during the production process in power plants leads to significant environmental challenges, highlighting the need for cleaner and renewable energy sources for electricity generation. Modern techniques, particularly the use of solar energy, can greatly reduce reliance on fossil fuels and natural gas, while also lowering labor costs. Additionally, government support and incentives aimed at developing infrastructure for clean energy can encourage farmers to adopt these technologies. In greenhouse cultivation, the majority of energy usage comes from natural gas, fuel, and electricity. Therefore, promoting intelligent control systems to manage environmental conditions—such as temperature, humidity, ventilation, and carbon dioxide levels—can significantly decrease energy consumption in this sector.