9th International Conference on ICT for Sustainable Development

Authors - Manoranjitham R, Yogesh K M, Alvino Rock, Vaishali R Kulkarni, Punitha S, Thompson Stephan
Abstract - This study addresses the significant impact of leaf diseases, such as rust and blast, on the agricultural productivity of pearl millet, a crop known for its high nutritional value and micronutrient content. In tackling these challenges, the research introduces a deep learning-based approach for the accurate and minimally supervised identification and diagnosis of these diseases. The methodology employs advanced deep learning algorithms, including VGG-16, Mobile Net V1, and Mobile Net V2, renowned for their pattern recognition capabilities and adaptability in applying knowledge from previous tasks to new scenarios. These mod-els are specifically utilized to detect rust and blast diseases in pearl millet. Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the effectiveness of these models. Results indicate a high level of accuracy, with the VGG-16 model achieving 99.45%, and both Mobile Net V1 and Mobile Net V2 models showing an accuracy of 99.32% in detecting diseased leaves. This research not only contributes to advancements in agricultural technology but also provides valuable tools for farmers and the agricultural industry to manage crop diseases more efficiently.