A Comprehensive Deep Learning Approach for Smart Crop Disease Diagnosis and Agricultural Decision Support
Keywords:
Deep Learning, Crop Disease Detection, Convolutional Neural Networks (CNNs), Transfer Learning, Image Processing, Precision Agriculture, Plant Pathology, Food Security, GAN-based Augmentation, Vision Transformers, Explainable AI, Edge Computing, IoT, Sustainable AgricultureAbstract
Global food security is critically challenged by plant diseases, which cause substantial economic losses and threaten agricultural productivity across diverse crop species. Conventional disease detection approaches, largely dependent on manual expert inspection, are inefficient, time-consuming, and susceptible to human error. The proliferation of artificial intelligence (AI) and deep learning (DL) technologies has opened promising avenues for the automated, accurate, and scalable detection and classification of crop diseases. This review paper provides a comprehensive synthesis of over 50 studies exploring the application of deep learning frameworks—particularly Convolutional Neural Networks (CNNs), transfer learning models, Generative Adversarial Networks (GANs), Vision Transformers, ensemble methods, and hybrid architectures—for plant disease detection using digital image processing. The review covers critical aspects including dataset development and augmentation strategies, model architectures, real-time mobile and IoT-based deployment, explain ability techniques, ethical considerations, and sustainability implications. A structured analysis of methodologies applied to diverse crops including wheat, rice, tomato, maize, potato, apple, and others is presented. The review identifies key challenges such as dataset scarcity, class imbalance, domain shift between laboratory and field environments, and limited model interpretability, and discusses emerging solutions. The paper concludes with a forward-looking research agenda that highlights the potential of federated learning, multimodal fusion, lightweight edge-deployable models, and integration with precision agriculture systems to transform the future of crop disease management.
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