Comparative Analysis of Deep Learning Models for Cotton Leaf Disease Detection

Cotton is the most essential crop and plays an important role in the agricultural economy of the country. Cotton crop is prone to many diseases because of changes in the climatic conditions, insects such as pink bollworm, and many other factors. These diseases decrease crop productivity, and at present farmers, are diagnosing the diseases with their own experience. But these kinds of naked-eye observations do not give accurate results on large plantation areas. Therefore, it is necessary to develop an automatic, accurate, and economic system for detecting leaf diseases. The aim of this work is to detect the infected cotton leaf using Convolutional Neural Network (ConvNet/CNN) which is a deep learning technique. Nearly 519 healthy leaves and 387 diseased leaves are collected from reliable sources and studied. This work focusses on the performance evaluation and comparison of the powerful CNN architectures: AlexNet, InceptionV3, and Residual Network (ResNet) 50, VGG 16, NASNet and Xception in detecting the diseased cotton leaf. Out of these six models, ResNet50 and VGG 16 has shown significant performance with 97.56% of accuracy.