CROP DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORK(CNN)

Crop diseases pose a significant threat to global food security and agricultural economies, causingsubstantial yield and quality losses. Traditional disease detection methods, reliant on manual visualinspection, are often subjective, time-consuming, and inaccessible to many smallholder farmers,especially in developing countries like Nigeria. The advent of smart farming andArtificialIntelligence (AI), particularly Convolutional Neural Networks (CNNs), of ers a promisingsolution.This study addresses the critical need for an ef icient, accurate, and scalable systemfor cropdiseasedetection. We aim to develop a CNN-based system capable of automatically identifyingandclassifying various crop diseases from leaf images. The research involves collectingandpreprocessing a diverse dataset of healthy and diseased plant leaf images, subsequentlybuildingand training a robust CNN model. The performance of the trained model will be rigorouslyevaluated using key metrics such as accuracy, precision, and recall. Finally, the study intendstodemonstrate the practical utility of the system through its deployment in a simple user interfaceormobile application.