Detecting Halyomorpha halys using a low-power edge-based monitoring system

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Kargar, Amin
Zorbas, Dimitrios
Tedesco, Salvatore
Gaffney, Michael
O'Flynn, Brendan
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Smart monitoring systems in orchards can automate agriculture monitoring processes and provide useful information about the presence of insects, such as the Brown Marmorated Stink Bug (BMSB), that threaten the production quantity and quality of fruit such as pears. Unlike other approaches in the literature, we propose a low-cost image monitoring system which exhibits a very low power consumption without compromising much of the accuracy that existing expensive systems incorporating significant computing and processing capability can achieve in such applications. The proposed system relies on a microcontroller unit and a camera which can take pictures of a double-sided sticky insect trap which, with the help of novel machine learning algorithms, can report on the presence of BMSB via a long-range communication link. The Internet of Things data capture and analysis system has recently been deployed in a real orchard in Italy which is subject to BMSB infestation and the first images have been analysed. This paper presents how the system works, the image processing, detection and classification algorithms, as well as a demonstration of the memory and energy consumption associated with the processing algorithms. The system achieves an accuracy of over 90% with multiple times less memory and energy consumption compared to other similar approaches in the literature.
Halyomorpha halys , Edge computing , Machine learning , Deep learning
Kargar, A., Zorbas, D., Tedesco, S., Gaffney, M. and O’Flynn, B. (2024) ‘Detecting Halyomorpha halys using a low-power edge-based monitoring system’, Computers and Electronics in Agriculture, 221, 108935. Available at:
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