Deep Learning-Driven Smart and Sustainable Agriculture

Deep learning (DL), a subfield of artificial intelligence (AI), is reshaping agriculture through data-driven, automated decision-making across the entire farming life cycle. Recent advancements in DL techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and Vision Transformers (ViTs), have demonstrated significant potential in soil analysis, crop monitoring, disease detection, and yield forecasting. DL models exhibit superior accuracy, scalability, and adaptability compared to traditional machine learning (ML) approaches. These capabilities are further enhanced by integration with enabling technologies, such as the Internet of Things (IoT), unmanned aerial vehicles (UAVs), and edge computing, facilitating real-time, field-deployable applications. Despite these innovations, several challenges persist, including data heterogeneity, limited rural infrastructure, and cybersecurity vulnerabilities. A critical examination of these challenges, along with a synthesis of recent technical progress and emerging trends, provides a foundation for developing smart and sustainable agricultural systems capable of addressing global food security and climate adaptation goals.