Abstract
Apple orchards are widely expanding in many countries of the world, and one of the major threats of these fruit crops is the attack of dangerous parasites such as the Codling Moth. IoT devices capable of executing machine learning applications in-situ offer nowadays the possibility of featuring immediate data analysis and anomaly detection in the orchard. In this paper, we present an embedded electronic system that automatically detects the Codling Moths from pictures taken by a camera on top of the insects-trap. Image pre-processing, cropping, and classification are done on a low-power platform that can be easily powered by a solar panel energy harvester.
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Acknowledgements
This research was supported by the IoT Rapid-Proto Labs projects, funded by Erasmus+ Knowledge Alliances program of the European Union (588386-EPP-1-2017-FI-EPPKA2-KA).
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Albanese, A., d’Acunto, D., Brunelli, D. (2020). Pest Detection for Precision Agriculture Based on IoT Machine Learning. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2019. Lecture Notes in Electrical Engineering, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-37277-4_8
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DOI: https://doi.org/10.1007/978-3-030-37277-4_8
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