Advancing Agriculture: Plant Disease Classification Through Cutting-Edge Deep Learning Techniques

The viability of agriculture and the security of the world's food supply are seriously threatened by plant diseases. Detecting these diseases promptly and accurately is crucial for effective disease control and minimizing crop output losses. Deep learning algorithms have shown possibilities recently as a method for accurately and automatically classifying plant diseases. This research presents an innovative deep-learning framework designed for plant disease classification, incorporating transfer learning and customized convolutional neural networks (CNNs). The proposed framework comprises three main phases: data pre-processing or transfer learning, feature extraction, and disease classification. This article presents a new approach to plant disease categorization using deep learning. It combines convolutional neural networks (CNNs) with transfer learning. Through this method, plant diseases can be identified with precision and automation across diverse plant species and types of disease. This facilitates more effective disease management, safeguarding the security of the global food supply. Comparative analysis indicates that the proposed method outperforms traditional approaches, yielding superior results.