TY - GEN
T1 - A Novel Resource-Constrained Insect Monitoring System based on Machine Vision with Edge AI
AU - Kargar, Amin
AU - Wilk, Mariusz P.
AU - Zorbas, Dimitrios
AU - Gaffney, Michael T.
AU - Q'Flynn, Brendan
N1 - Funding Information:
This project is co-funded by the European Regional Development Fund (ERDF) under Ireland’s European Structural and Investment Funds Programmes 2014–2020. This work was carried out as part of the Haly.ID project – 2020EN508 funded by Ireland’s Department of Agriculture, Food and the Marine under Grant: 2020 Trans National ERA-NET. The first author is supported by a Walsh Scholarship funded by Teagasc, The Irish Food and Agriculture Authority. Aspects of this work have been supported by Science Foundation Ireland under Grant 12/RC/2289-P2-INSIGHT, 13/RC/2077-CONNECT, 16/RC/3835-VISTAMILK and 16/RC/3918-CONFIRM which are co-funded by the European Regional Development Fund (ERDF) under Ireland’s European Structural and Investment Funds Programmes 2014-2020.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Deep Learning
KW - Edge AI
KW - Food Security
KW - Image processing
KW - Integrated Pest Monitoring
KW - Machine Vision
UR - http://www.scopus.com/inward/record.url?scp=85149772714&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85149772714&partnerID=8YFLogxK
U2 - 10.1109/IPAS55744.2022.10052895
DO - 10.1109/IPAS55744.2022.10052895
M3 - Conference contribution
AN - SCOPUS:85149772714
T3 - 5th IEEE International Image Processing, Applications and Systems Conference, IPAS 2022
BT - 5th IEEE International Image Processing, Applications and Systems Conference, IPAS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Image Processing, Applications and Systems Conference, IPAS 2022
Y2 - 5 December 2022 through 7 December 2022
ER -