Optimal Training Ensemble of Classifiers for Classification of Rice Leaf Disease

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