CROP DISEASE DETECTION USING CONVOLUTION NEURAL NETWORK

Abstract

Plant disease is a persistent problem for smallholder farmers, threatening their income and food security. The current surge in smartphone adoption and computer vision models has presented an opportunity for picture categorization in agriculture. Convolutional Neural Networks (CNNs) are regarded cutting-edge in image recognition, with the capacity to produce rapid and precise results. The efficacy of a pre-trained ResNet34 model in identifying crop disease is examined in this Project. The proposed model, which is implemented as a mobile application, is capable of distinguishing several plant illnesses from healthy leaftissue. A dataset is created in a controlled environment for training and evaluating the model. The validation findings suggest that the suggested approach is capable of achieving accuracy. This illustrates the technological viability of CNNs in diagnosing plant illnesses and paves the way for AI solutions for small-scale farmer