PLANT DISEASE DETECTION AND CLASSIFICATION USING DEEP LEARNING

The Plant diseases significantly impact agricultural productivity, necessitating efficient and accurate detectionmethods. Traditional disease identification methods rely on manual inspection, which is time-consuming and prone toerrors. Recent advancements in deep learning have enabled automated and precise plant disease detection andclassification. This paper presents a deep learning-based approach utilizing Convolutional Neural Networks (CNNs)to identify and categorize plant diseases from leaf images. The proposed model is trained on a diverse dataset of plantleaf images and leverages transfer learning to enhance classification accuracy. Experimental results demonstrate highaccuracy in detecting multiple plant diseases, outperforming traditional machine learning methods. The findingshighlight the potential of deep learning in revolutionizing precision agriculture by providing real-time, automated, andscalable disease detection solutions. Experimental results highlight the effectiveness of the approach, achievingclassification accuracy exceeding 90% on benchmark dataset.Future work includes expanding the dataset, optimizingmodel performance, and integrating the system with mobile and IoT-based solutions for real-world deployment.