Analysis of Pre-Trained CNN Models for Pepper and Potato Leaf Disease Prediction
V. UmaRani, S. Thirisaa
Abstract
Agriculture serves as a backbone for human life as well as for the economic development of the country. Major loss in agriculture is mainly due the disease which occurs on the plants. Crop diseases pose a significant threat to global food security, affecting the yield and quality of agricultural produce. Early detection of plant diseases is crucial for implementing timely interventions and minimizing crop losses. This research analyses the pretrained model to automate the detection of diseases in pepper and potato plants based on leaf images. This research analyzes the images of healthy and diseased pepper and potato leaves. The popular pre-trained CNN models VGG-16, Inception-v3 and Resnet are compared and finds the best algorithm for pepper and potato leaf disease prediction. According to experimental analysis, the Resnet-50 has obtained highest accuracy (100%), followed by VGG-16 (99 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) and Inception-v3 (96 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ).