AI-Based Deep Learning Framework for Early Detection and Classification of Crop Diseases

Accurate and timely identification of plant diseases is essential for improving agricultural productivity and minimizing crop losses. Traditional methods rely on manual inspection, which is often time-consuming, inconsistent, and dependent on expert knowledge. This paper presents a deep learning-based approach for automated crop disease prediction using leaf images. The proposed system utilizes convolutional neural networks, with a customized residual architecture designed to effectively capture complex visual patterns associated with different plant diseases. A publicly available dataset containing multiple plant species and disease categories is used for training and evaluation. The images are preprocessed through resizing and normalization to ensure consistency. The model is trained using optimized techniques, including adaptive optimization and dynamic learning rate scheduling, to enhance performance and generalization. Experimental results demonstrate that the proposed approach achieves high classification accuracy across multiple disease classes. In particular, the residual network model attains a validation accuracy of 99.2%, outperforming other implemented architectures in both accuracy and stability. The system also shows strong generalization capability when tested on unseen data. The proposed method provides a reliable and efficient solution for automated plant disease detection, with potential applications in smart agriculture and precision farming.