Hybrid CNN-Wavelet Approach in Detecting Plant Disease and Classifications

Agriculture forms the backbone in a country like India, where the greater part of the population engages itself as farmers. Data-driven technologies, deep learning techniques, and CNNs have thus proved to be great improvement tools for agriculture. Introducing wavelet transformations with CNN for plant disease detection and classification will result in an enhanced technique that is presented in this paper. The proposed method identifies and predicts diseases like apple scab, cercospora leaf spot, late blight, and powdery mildew by using a dataset of images of plant leaves. Wavelet transformations are used to preprocess the input images, and they decompose the images into multi-resolution components that highlight critical features such as texture and patterns associated with disease symptoms. This preprocessing step enhances the ability of the CNN to extract meaningful features, improves the accuracy of prediction, and makes it robust, even with noisy or low-quality data. Early detection, which is facilitated by this method, leads to timely treatment and control, thereby reducing crop losses among farmers. The results confirm that the integration of wavelet transformations with CNN models provides a reliable, efficient, and intelligent tool for disease classification. This approach can assist farmers and decision-makers to improve agricultural yields, reduce losses, and thus enhance food security.