مجلة المنتدى للدراسات والابحاث الاقتصادية
Volume 9, Numéro 2, Pages 730-750
2025-12-07

Deep Learning Uses In Rural Development And Agriculture: Towards Intelligent, Sustainable Food Systems



Authors : Selt Mohammed Mostefa .

Abstract

The rapid progress of deep learning (DL) has revolutionized diverse fields including computer vision, natural language processing, and autonomous systems. Agriculture, a traditionally labor-intensive and risk-prone sector, is now being transformed through the integration of DL technologies capable of analyzing high-dimensional, multi-source data to optimize decision-making. This paper explores the state-of-the-art applications of deep learning in agriculture and its broader implications for rural development. By reviewing key models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid transformer-based architectures, we examine their role in crop and field monitoring, disease detection, yield prediction, irrigation management, and agricultural robotics. Beyond technical applications, the paper highlights how DL-driven innovation contributes to rural livelihoods by enhancing productivity, reducing risks, and promoting sustainable resource management. However, several challenges—ranging from data scarcity and infrastructural constraints to ethical and institutional issues—continue to impede the widespread adoption of these tools in low-resource settings. The study concludes with research and policy recommendations to bridge the gap between technological potential and practical impact, emphasizing inclusive, transparent, and participatory approaches to digital agriculture.

Keywords

: Deep learning ; Artificial intelligence ; Smart agriculture ; Precision farming ; Digital transformation ; Rural development

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