Mango Pest Detection Using Entropy-ELM with Whale Optimization Algorithm

Abstract

Image processing, agricultural production, and field monitoring are essential studies in the research field. Plant diseases have an impact on agricultural production and quality. Agricultural disease detection at a preliminary phase reduces economic losses and improves the quality of crops. Manually identifying the agricultural pests is usually evident in plants; also, it takes more time and is an expensive technique. A drone system has been developed to gather photographs over enormous regions such as farm areas and plantations. An atmosphere generates vast amounts of data as it is monitored closely; the evaluation of this big data would increase the production of agricultural production. This paper aims to identify pests in mango trees such as hoppers, mealybugs, inflorescence midges, fruit flies, and stem borers. Because of the massive volumes of large-scale high-dimensional big data collected, it is necessary to reduce the dimensionality of the input for classifying images. The community-based cumulative algorithm was used to classify the pests in the existing system. The proposed method uses the Entropy-ELM method with Whale Optimization to improve the classification in detecting pests in agriculture. The Entropy-ELM method with the Whale Optimization Algorithm (WOA) is used for feature selection, enhancing mango pests’ classification accuracy. Support Vector Machines (SVMs) are especially effective for classifying while users get various classes in which they are interested. They are created as suitable classifiers to categorize any dataset in Big Data effectively. The proposed Entropy-ELM-WOA is more capable compared to the existing systems.