Rapid detection and identification of major vegetable pests based on machine learning Online publication date: Tue, 09-Aug-2022
by Changzhen Zhang; Yaowen Ye; Deqin Xiao; Long Qi; Jianjun Yin
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 22, No. 3/4, 2022
Abstract: To develop strategies for vegetable pest control, information on the type of pests and the quantity of the pests is essential. In this study, an automatic pest monitoring system has been developed by combining remote information processing technology and machine vision technology. A Vegetable Pest Counting Algorithm Based on Machine Learning (VPCA-ML) was proposed and implemented in a vegetable field to monitor four major pests: Phyllotretastriolata (F.), Frankliniella occidentalis (P.), Bemisiatabaci (G.) and Plutellaxylostella (L.). Results show that a bag-of-feature model in the algorithm is feasible for representing pest features, and an improved SVM model is suitable for pest classifications. Compared with the manual counts, the VPCA-ML results in an overall relatively error of less than 10%. The system based on the VPCA-ML can quickly and accurately acquire the types and dynamic quantities of major vegetable pests, and have a stable operation in a field environment.
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