Adaptive fusion of K-means region growing with optimized deep features for enhanced LSTM-based multi-disease classification of plant leaves

The manual process of plant leaves disease detection takes more time to perform. To achieve successful classification results, a flawless feature extraction is required for model. Aiming at the localization diseased-plant leaves, this paper performs complex tasks like segmentation, and multi-disease using ‘improved extraction, classification’ models. Here, Adaptive Fusion K-Means Region Growing (AFKMRG) accomplishes abnormality segmentation leaves. extracted features are subjected Enhanced Long short-term memory (LSTM) performing classification. classification, improved by Fitness Sorted Jaya-Forest Optimization Algorithm (FSJ-FOA). From empirical accuracy precision designed method attains 98.35% 98.40% all kinds Results show that provides elevated performance with diverse metrics.