Deep Learning LSTM Approach on Hyperspectral Images using Keras Framework

Cotton is one of Ethiopia's most commercially important agricultural crops, although it faces a variety of challenges in the leaf area. The bulk of these limitations are caused by diseases and pests that are difficult to spot with the naked eye. Using the deep learning approach CNN, this research aimed at developing a model to improve the detection of cotton leaf disease by insects. Researchers have done this using common diseases of the cotton leaf and insects such as bacterial blight, spider mite, and leaf miner. K -variance verification method was used to classify the databases and improved the performance of the CNN model as a whole. In this study, approximately 2400 specimens of 600 images per class were present retrieved.