In this work, inspired by the needs of the H2020 European project PANTHEON for the precision farming of hazelnut orchards, we propose a data-driven pest detection system. Indeed, the early detection of pests represents an essential step towards the design of effective crop defense strategies in Precision Agriculture (PA) settings. Among the possible pests, we focus on true bugs as they can heavily compromise hazelnut production. To this aim, we collect a custom dataset in a realistic outdoor environment and train a You Only Look Once (YOLO)-based Convolutional Neural Network (CNN), achieving ≈ 94.5% average precision on a holdout dataset. We extensively evaluate the detector performance by also analyzing the influence of data augmentation techniques and of depth information. We finally deploy it on a NVIDIA Jetson Xavier on which it reaches ≈ 50 fps, enabling online processing on-board of any robotic platform.

Lippi, M., Bonucci, N., Carpio, R.F., Contarini, M., Speranza, S., Gasparri, A. (2021). A YOLO-based pest detection system for precision agriculture. In 2021 29th Mediterranean Conference on Control and Automation, MED 2021 (pp.342-347). Institute of Electrical and Electronics Engineers Inc. [10.1109/MED51440.2021.9480344].

A YOLO-based pest detection system for precision agriculture

Lippi M.;Bonucci N.;Carpio R. F.;Gasparri A.
2021

Abstract

In this work, inspired by the needs of the H2020 European project PANTHEON for the precision farming of hazelnut orchards, we propose a data-driven pest detection system. Indeed, the early detection of pests represents an essential step towards the design of effective crop defense strategies in Precision Agriculture (PA) settings. Among the possible pests, we focus on true bugs as they can heavily compromise hazelnut production. To this aim, we collect a custom dataset in a realistic outdoor environment and train a You Only Look Once (YOLO)-based Convolutional Neural Network (CNN), achieving ≈ 94.5% average precision on a holdout dataset. We extensively evaluate the detector performance by also analyzing the influence of data augmentation techniques and of depth information. We finally deploy it on a NVIDIA Jetson Xavier on which it reaches ≈ 50 fps, enabling online processing on-board of any robotic platform.
978-1-6654-2258-1
Lippi, M., Bonucci, N., Carpio, R.F., Contarini, M., Speranza, S., Gasparri, A. (2021). A YOLO-based pest detection system for precision agriculture. In 2021 29th Mediterranean Conference on Control and Automation, MED 2021 (pp.342-347). Institute of Electrical and Electronics Engineers Inc. [10.1109/MED51440.2021.9480344].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/393864
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