Image Based Plant Disease Detection Using CNN With Optimized e-Commerce.

Agriculture plays a crucial role in the global economy, and plant diseases significantly impact crop yield and food security. Traditional disease detection methods are time-consuming, require expert knowledge, and are often ineffective in large-scale farming. To address these challenges, AI-based plant disease detection using Convolutional Neural Networks (CNNs) offers an automated, accurate, and efficient solution. This study explores the application of deep learning, particularly CNNs, for detecting plant diseases from leaf images. The model is trained on a large dataset of diseased and healthy plant images, preprocessed using techniques such as image augmentation and normalization. CNN architectures, including ResNet, VGG, and MobileNet, are evaluated for their classification performance. The system identifies diseases based on unique leaf patterns, color changes, and texture variations, achieving high accuracy with minimal human intervention. The proposed approach enhances early disease detection, reducing economic losses and promoting sustainable farming practices. Real-time implementation can be achieved through mobile applications, enabling farmers to capture leaf images and receive instant diagnoses. Furthermore, integration with cloud-based systems allows continuous model improvement through new data.