Chilli Leaf Disease Prediction System using Deep Learning Model

A vital part of any nation's economy, agriculture provides farmers and their families with the resources they need. Maintaining food security requires the cultivation of chillies, a crucial source of income. When cultivating chillies, a variety of pests and illnesses can result in large losses in yield. Crop illnesses are a serious threat to farmers livelihoods, which emphasizes how critical it is to identify diseases in chilli plants early on at the appropriate stage of growth in order to provide fertilizer on time.The health of the chilli plant is assessed by taking and analyzing a picture of its leaves. A deep learning model called a convolutional neural network (CNN) architecture is made to process structured, grid-like data, like pictures. Convolutional, pooling, and fully linked layers are among its several layers. CNNs hierarchical feature extraction capabilities make them extremely effective for tasks like object identification, picture segmentation, and image classification. Using the dataset of chilli leaves, a pretrained deep learning network EfficientNetB0 was used to identify and analyze the diseases (leaf curl, leaf spot, white fly, and yellowish leaf).