نویسندگان
1 گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران.
2 Tabriz
3 دانشجوی دکتری مهندسی برق دانشگاه تبریز
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Artificial intelligence has wide applications in various industries, especially the agricultural industry, which significantly helps to increase productivity, reduce costs and improve services. This paper provides a comprehensive review of recent research on product performance prediction using artificial intelligence algorithms. A wide range of machine learning methods, tools, data mining, existing challenges and limitations, and datasets that have been used to predict the performance of various products are reviewed. Focusing on a wide range of crops, including rice, wheat, sugarcane, and soybean, reviews emphasize the importance of crop yield prediction in precision agriculture and farmer decision-making. The use of machine learning algorithms including supervised and unsupervised methods, such as regression, decision trees, support vector machines and deep models such as artificial neural networks, are discussed in this research. Several tools such as TensorFlow, Keras and Scikit-learn have been used to develop and test these models. Data mining helps to extract meaningful patterns from vast agricultural data. Also, existing challenges and limitations such as data quality, interpretation of models and the need to adapt to the specific conditions of the agricultural field have also been investigated. The results show that compared to traditional statistical methods, machine learning models, especially artificial neural networks, have higher accuracy in They predict the performance of products. This paper concludes that the adoption of artificial intelligence in agriculture will not only increase the accuracy of yield prediction, but also support informed decision-making and lead to overall improvements in agricultural productivity and sustainability.
کلیدواژهها [English]
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