Automatic Classification of Real-Time Diseased Cotton Leaves and Plants Using a Deep-Convolutional Neural Network

Abstract

The automated detection and classification of plant diseases based on images of leaves is a significant milestone in agriculture. In this paper, the concept of deep learning was used to identify and predict cotton plant disease status using real-time images of leaves and plants. The models were trained using a database of 2293 images of cotton leaves and plants. The data included four distinct classes of leaves, plants disease combinations, and their respective categories. Python version 3.6.9 is used to implement the developed model. The model includes a deep learning package such as Keras, TensorFlow, and a Googlecolab cloud-based jupyter notebook as the development environment. 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 time spent determining its severity.

An Intelligent Approach for Cotton Plant Disease Detection using Convolutional Neural Networks: A Deep Learning Perspective

“…The study [20] talks about a big achievement in farming -using computers to automatically find and classify diseases in plants by looking at pictures of their leaves. They concentrated on cotton plants in this instance.…”

CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

“…To find out the status of cotton plant diseases using real-time samples of plants and leaves, the conception of deep learning is utilized. The model consists of deep learning packages including TensorFlow, Keras, and Googlecolab [30]. To boost the recognition process of pests the researchers utilized CNN [31].…”

“…Utilizing the transfer learning with Mask RCNN while decreasing the loss value due to increased optimized iterations, achieved an accuracy of 94% [29]. The researcher proposed a CNN framework that is encouraged by the AlexNet framework for the sake of the classification of healthy and diseased cotton plants and leaves with an accuracy of 97.98% [30]. For cotton disease detection and pest recognition, the researchers utilized CNN deep learning technique is utilized which gives an accuracy of 96.4% [32].…”