Development of a Low-Power Automatic Monitoring System for Spodoptera frugiperda (J. E. Smith)

Meixiang Chen, Liping Chen, Tongchuan Yi, Ruirui Zhang (), Lang Xia, Cheng Qu, Gang Xu, Weijia Wang, Chenchen Ding, Qing Tang and Mingqi Wu
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Meixiang Chen: National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
Liping Chen: National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
Tongchuan Yi: National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
Ruirui Zhang: National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
Lang Xia: National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
Cheng Qu: Institute of Plant Protection, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Gang Xu: National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
Weijia Wang: National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
Chenchen Ding: National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
Qing Tang: National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
Mingqi Wu: National Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China

Agriculture, 2023, vol. 13, issue 4, 1-19

Abstract: Traditional traps for Spodoptera frugiperda (J. E. Smith) monitoring require manual counting, which is time-consuming and laborious. Automatic monitoring devices based on machine vision for pests captured by sex pheromone lures have the problems of large size, high power consumption, and high cost. In this study, we developed a micro- and low-power pest monitoring device based on machine vision, in which the pest image was acquired timely and processed using the MATLAB algorithm. The minimum and maximum power consumption of an image was 6.68 mWh and 78.93 mWh, respectively. The minimum and maximum days of monitoring device captured image at different resolutions were 7 and 1486, respectively. The optimal image resolutions and capture periods could be determined according to field application requirements, and a micro-solar panel for battery charging was added to further extend the field life of the device. The results of the automatic counting showed that the counting accuracy of S. frugiperda was 94.10%. The automatic monitoring device had the advantages of low-power consumption and high recognition accuracy, and real-time information on S. frugiperda could be obtained. It is suitable for large-scale and long-term pest monitoring and provides an important reference for pest control.

Keywords: low-power consumption; machine vision; automatic monitoring; sex pheromone; identification (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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