Identification of Banana Leaf Diseases and Detection

In modern agriculture, there is a growing interest and attention concentrated on the diagnosis of plant diseases. Recognizing early signs and symptoms of a disease can help to keep outbreaks of the disease on crops to a minimum. Aside from that, most currently available solutions rely on manual identification by a small number of experts, which waste both time and money. Certain artificial intelligence technologies are now being used to provide faster detection and identification of diseases while requiring less work and expenses. This research presents a proposed methodology for the analysis and identification of banana leaf diseases that makes use of Convolutional Neural Networks (CNN). The experimental results demonstrated that the suggested method is capable of providing satisfactory detection and classification of three major banana leaf diseases, namely Cordana, Pestalotiopsis, and Sigatoka, with the validation accuracy of up to 96.24%