A novel resource-constrained insect monitoring system based on machine vision with edge AI

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Kargar, Amin
Wilk, Mariusz P.
Zorbas, Dimitrios
Gaffney, Michael T.
O'Flynn, Brendan
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Institute of Electrical and Electronics Engineers (IEEE)
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Effective insect pest monitoring is a vital component of Integrated Pest Management (IPM) strategies. It helps to support crop productivity while minimising the need for plant protection products. In recent years, many researchers have considered the integration of intelligence into such systems in the context of the Smart Agriculture research agenda. This paper describes the development of a smart pest monitoring system, developed in accordance with specific requirements associated with the agricultural sector. The proposed system is a low-cost smart insect trap, for use in orchards, that detects specific insect species that are detrimental to fruit quality. The system helps to identify the invasive insect, Brown Marmorated Stink Bug (BMSB) or Halyomorpha halys (HH) using a Microcontroller Unit-based edge device comprising of an Internet of Things enabled, resource-constrained image acquisition and processing system. It is used to execute our proposed lightweight image analysis algorithm and Convolutional Neural Network (CNN) model for insect detection and classification, respectively. The prototype device is currently deployed in an orchard in Italy. The preliminary experimental results show over 70 percent of accuracy in BMSB classification on our custom-built dataset, demonstrating the proposed system feasibility and effectiveness in monitoring this invasive insect species.
Integrated Pest Management (IPM) , Machine vision , Image processing , Deep learning , Edge AI , Integrated pest monitoring , Food security
Kargar, A., Wilk, M. P., Zorbas, D., Gaffney, M. T. and O'Flynn, B. (2023) 'A novel resource-constrained insect monitoring system based on machine vision with edge AI', 2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS), Genova, Italy, 5 - 7 December, pp. 1-6. doi: 10.1109/IPAS55744.2022.10052895
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