Evolution of Deep Learning Approaches in Crop Disease Detection: A Review

Agriculture plays a key role in India’s economy, and plant diseases significantly impact the crop yield and the food security throughout the world. Conventional methods for disease identification, relying on expert knowledge, are often slow and less reliable, necessitating advanced technological solutions for precise and early detection. This paper highlights advancements in agricultural image recognition, classification, and real-time disease management. It shows the use of different deep learning architectures such as GoogleNet, AlexNet, ResNet and EfficientNet have been applied to detect diseases in major crops, including rice, wheat, maize, potatoes and apples. It also discusses challenges such as data availability, image quality, and computational complexity are discussed along with strategies like transfer learning, data augmentation and feature extraction. Review analysis discovers important issues, including the requirement for substantial training datasets, environmental unpredictability, and the integration of different data sources. The study accomplishes by highlighting the potential of deep learning in developing automated, scalable, and cost-effective solutions for plant disease identification with potential future paths in this direction.