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

Abstract

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

Keywords

• multi-disease classification
• adaptive fusion of k-means region growing
• deep learning
• fitness sorted-based jaya and forest optimization algorithm