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Artificial intelligence in agriculture: A review of recent advances, challenges, and opportunities | ||
| Biosystems Engineering and Renewable Energies | ||
| دوره 1، شماره 2، آذر 2025، صفحه 157-164 اصل مقاله (466.08 K) | ||
| نوع مقاله: Review Papers | ||
| شناسه دیجیتال (DOI): 10.22069/bere.2026.24438.1039 | ||
| نویسندگان | ||
| Tayyeb Nazghelichi* 1؛ Mohammadhadi Ghafari2؛ Abolfazl Aghajanlou3 | ||
| 1Department of Biosystems Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 2University of Urmia, Urmia, Iran | ||
| 3Department of Technology and Agriculture, Faculty of Aburaihan, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| Global agriculture faces profound challenges, including resource scarcity, climate change, and a 70% increase in food demand by 2050, resulting in 20-40% annual crop losses. Artificial intelligence (AI) is a key transformative force, integrating machine learning (ML), deep learning (DL), and the Internet of Things (IoT) to deliver data-driven solutions for precision agriculture, enhancing productivity, sustainability, and resilience. This systematic review, based on a comprehensive search in databases like Web of Science, Scopus, and Google Scholar focusing on peer-reviewed English-language articles, analyzes AI applications in crop yield prediction (with over 96% accuracy via satellite imagery), pest and disease detection (using CNNs), soil and environmental monitoring (with IoT sensors), market price forecasting (with LSTM), and smart mechanization (such as autonomous tractors). In Iran, AI shows promise in managing strategic crops (such as wheat using RF and SVM), soil assessment (erosion mapping), and livestock (disease prediction), though challenges like data scarcity, weak infrastructure, and socioeconomic barriers hinder its expansion. Research gaps include insufficient integration with post-harvest management, a lack of longitudinal studies, and the absence of ethical standards. This paper, by proposing future paths such as interdisciplinary collaboration and supportive policies, emphasizes AI's potential for achieving sustainable agriculture and food security, boosting national food security by up to 20%, positioning Iran as a regional leader in the next generation of AgriTech. | ||
| کلیدواژهها | ||
| Deep learning؛ Digital divide in agriculture؛ Machine learning؛ Precision agriculture؛ Sustainable smart farming | ||
| مراجع | ||
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