Classification of Diseased Cotton Leaves and Plants Using Improved Deep Convolutional Neural Network

The automated detection and classification of plant diseases based on images of leaves is a significant milestone in agriculture. Due to the increasing popularity of digital image processing, machine learning, and computer vision techniques, it has been proposed that these could be used for the early detection of diseases. However, the accuracy of these techniques is still considered to be a challenge. In this paper, the concept of deep learning was used to identify and predict cotton plant disease status using images of leaves and plants collected in an uncontrolled environment. This paper focuses on solving the problem of cotton plants disease detection and classification using an improved Deep Convolution Neural Network based model. Three different experimental configurations were investigated to study the impacts of different data split ratios, different choices of pooling layer (max-pooling vs. average-pooling), and epoch sizes. The models were trained using a database of 2293 images of cotton leaves and plants. The data included four distinct classes of leaves, plant disease combinations, and their respective categories. For classifying leaves and plant diseases in cotton plants, our model attained an accuracy of 97.98%. The proposed technique outperformed the recent approaches indicated in earlier literature for relevant parameters. As a result, the technique is intended to reduce the time spent identifying cotton leaf disease in significant production regions and human error and the time spent determining its severity.