Optimal Training Ensemble of Classifiers for Classification of Rice Leaf Disease

is one of the most extensively cultivated crops in India. Leaf diseases can have a significant impact on the productivity and quality of a rice crop. Since it has a direct impact on the economy and food security, the detection of rice leaf diseases is the most important factor. The most prevalent diseases affecting rice leaves are leaf blast, brown spots, and hispa. To address this issue, this research builds a new classification model for rice leaf diseases. The model begins with a preprocessing step that employs the Median Filter (MF) process. Improved BIRCH is then utilized for picture segmentation. Features such as LBP, GLCM, color, shape, and modified Median Binary Pattern (MBP) are retrieved from segmented images. Then, an ensemble of three classification models, including Bi-GRU, Convolutional Neural Network (CNN), and Deep Maxout (DMN) is utilized. By adjusting the model weights, the suggested Opposition Learning Integrated Hybrid Feedback Artificial and Butterfly algorithm (OLIHFABA) will train the model to improve the performance of the proposed work.