Optimization with Deep Learning Classifier-Based Foliar Disease Classification in Apple Trees Using IoT Network

Abstract

The development of any country is influenced by the growth in the agriculture sector. The prevalence of pests and diseases in plants affects the productivity of any agricultural product. Early diagnosis of the disease can substantially decrease the effort and the fund required for disease management. The Internet of Things (IoT) provides a framework for offering solutions for automatic farming. This paper devises an automated detection technique for foliar disease classification in apple trees using an IoT network. Here, classification is performed using a hybrid classifier, which utilizes the Deep Residual Network (DRN) and Deep [Formula: see text] Network (DQN). A new Adaptive Tunicate Swarm Sine–Cosine Algorithm (TSSCA) is used for modifying the learning parameters as well as the weights of the proposed hybrid classifier. The TSSCA is developed by adaptively changing the navigation foraging behavior of the tunicates obtained from the Tunicate Swarm Algorithm (TSA) in accordance with the Sine–Cosine Algorithm  (SCA). The outputs obtained from the Adaptive TSSCA-based DRN and Adaptive TSSCA-based DQN are merged using cosine similarity measure for detecting the foliar disease. The Plant Pathology 2020 — FGVC7 dataset is utilized for the experimental process to determine accuracy, sensitivity, specificity and energy and we achieved the values of 98.36%, 98.58%, 96.32% and 0.413 J, respectively.