ELITE STOCHASTIC OPTIMIZED DISTRIBUTED LEARNING BASED ENSEMBLE MODEL FOR TOMATO LEAF DISEASE DETECTION AND CLASSIFICATION

Abstract

Tomato leaf disease is a primary factor impacting the quality and quantity of crop yield. The rapid spread of diseases and inefficient production have driven diverse models for classifying diseases. Nevertheless, existing traditional models are constrained by poor robustness, limited generalization, and inconsistent performance. This paper proposes an Elite Stochastic Optimized Distributed  Learning-Based Ensemble Deep Neural Network Long Short-Term Memory (ESD2TM) model for effective disease detection and classification. The ESD2TM framework utilized a distributed mirrored strategy featuring a replica model  which engaged in parallel training to boost the training speed and reduce significant computational requirements. In addition, the Hybrid Mutual Augmented Structural Features (HMAS) technique effectively gathers the context-aware characteristics and spatial relationships within the leaves to determine the irregularities based on the disease symptoms. In addition, the Elite Stochastic Optimization Algorithm (ElSTO) refines the hyper parameters and exhibits balanced diversity to find the optimal solution. The incorporation of these mechanisms enables the ESD2TM approach to achieve an accuracy, specificity, precision and sensitivity of 98.42%, 98.46%, 97.69%, and 98.39%, respectively.