Deep Learning Approaches for Plant Diseases Identification and Classification: A Comprehensive Review

Plant diseases have a significant impact on agricultural productivity and global food security. Accurate and timely identification of plant diseases is crucial for effective disease management.In recent years, deep learning (DL) techniques, particularly convolutional neural networks (CNNs), have emerged as powerful tools for automated plant disease identification and classification. This comprehensive review aims to provide an in-depth analysis of the current state of deep learning-based approaches for plant disease diagnosis.We discuss the advantages of deep learning in learning discriminative features from raw image data, explore various model architectures, transfer learning strategies, data augmentation techniques, challenges related to labeled datasets, performance evaluation metrics, and limitations. Furthermore, we highlight the potential impact of deep learning in precision agriculture and its implications for crop health monitoring and disease management.