Raspberry Pi Camera-Based Leaf Pest Detection By Using Classical Image Processing With CNN Algorithm

This research describes the creation of a sophisticated system that combines convolutional neural networks (CNNs) with traditional image processing methods to identify pests and illnesses on plant leaves. The primary goal is to increase agricultural output by facilitating early identification and offering prompt treatment suggestions, which will lower crop losses. The system initially employs traditional image processing methods to identify the edges of leaves, improve image contrast, and detect anomalies such as black spots, pests, and other foreign objects. These methods utilize various techniques, including color space conversion (from BGR to HSV), adaptive thresholding, morphological operations, and contour detection, which help the system isolate and highlight potential problem areas on the leaf. Although these traditional methods are effective, they have limitations in accurately detecting complex patterns, especially under varying lighting conditions and across different leaf types. To address these difficulties, the project incorporates Convolutional Neural Networks (CNNs), which are advanced machine learning models that can autonomously acquire and extract characteristics from large collections of leaf pictures. The CNN model is trained on a comprehensive dataset that includes photos of both healthy and sick leaves. Data augmentation methods are used to enhance its resilience. The model’s architecture comprises convolutional layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for detection. A comparative analysis between the CNN-based approach and traditional image processing methods demonstrates significant enhancements in accuracy, robustness, and scalability. The CNN model is particularly effective in identifying even subtle disease patterns and pests, offering a more adaptable and thorough solution for real-time leaf disease detection. This system is valuable for farmers, facilitating early intervention and more effective pest and disease management in crops.