A PLANT LEAF DISEASE IMAGE DETECTION AND CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS (CNN) AND OPENCV

Abstract

This paper addresses the challenge of accurately classifying plant leaf diseases by proposing a novel deep learning approach based on Convolutional Neural Networks (CNN). Traditional CNN models often struggle to effectively capture the spatial and posture relationships of plant disease lesions, leading to issues with recognition accuracy and robustness. To overcome this limitation, we introduce an optimized CNN architecture designed specifically for plant leaf disease image classification. The proposed system enhances feature extraction by incorporating advanced convolutional layers that better capture the fine-grained details of leaf lesions. Additionally, a channel attention mechanism is integrated into the network to improve its focus on the most critical features associated with disease detection. To further improve performance, the architecture is designed to handle image transformations such as rotations, scaling, and flipping, ensuring the model's robustness across diverse real-world conditions. In addition to classification, the proposed approach also incorporates disease lesion detection using OpenCV. By utilizing OpenCV for image processing, such as drawing bounding boxes around given image, the model not only classifies the diseases but also locates them accurately the plant leaf images. This step enhances the interpretability of the model and provides more detailed information about the affected regions, which can be useful for precision agriculture applications. The model is trained and tested on multiple plant disease datasets, demonstrating significant improvements in classification accuracy, robustness, and generalization compared to traditional CNN models. The proposed method provides a reliable and efficient solution for automatic plant disease diagnosis, offering significant potential for agricultural applications and crop management.