CottonLeafNet: cotton plant leaf disease detection using deep neural networks

Abstract

India is a cover crop region whereby agricultural production sustains a substantial proportion of the populace and upon which the whole Indian economy is heavily reliant. As per research, it provides subsistence for around 70% of rural households. In terms of agricultural output and exports, India ranks second and ninth, respectively. However, it accomplishes the first position globally in terms of cotton exports thereby adequately contributing to the economy of the country. However, it has been documented that various crops especially cotton plants are severely harmed by various pests, extreme climatic variations, nutrient inadequacy and toxicity, and so on. Cotton plant diseases cause a wide range of illnesses ranging from bacterial to nutritional deficiency giving a hard time for the human eye to recognize. However, most of the researchers have considered only a few types of cotton leaf diseases and excluded many. Keeping these constraints in consideration, this research seeks to aid the detection of these diseases by employing deep learning paradigms. The research begins with acquiring a near-balanced dataset with 22 leaf disease types including bacterial, fungal, viral, nutrient deficiency, etc. followed by data augmentation to boost the performance of the models. Many algorithms were tested, however, CNN happens to be very efficient and productive. The proposed model when evaluated on the test set achieves an accuracy of 99.39% with a negligible error rate, thus outperforming all the existing approaches by consuming less computational time. The outcome portrays that the proposed approach has the efficiency to be implemented in real-time detection systems to aid the precise detection of cotton leaf diseases to help the farmers in taking appropriate actions. � 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.