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

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.