Identifying pests and treating them in a timely manner is a crucial aspect of the Precision Agriculture (PA) paradigm. Driven by the needs of the H2020 European Project Pantheon, focused on precision farming in hazelnut orchards, we propose a pest management system for gall-mites, which cause severe symptoms on generative and vegetative buds. We develop a data-driven monitoring system based on You Only Look Once (YOLO) framework enabling the early detection of the pest infestation with mean average precision of 86.7% on a holdout dataset. We perform a thorough analysis on its performance using several data augmentation methods as well as validate its real-time computation capability on a NVIDIA Jetson Xavier, which can be easily integrated into any robotic platform. Finally, we contextualize the role of the proposed detection framework in a comprehensive pest management system.

Lippi, M., Carpio, R.F., Contarini, M., Speranza, S., Gasparri, A. (2022). A Data-Driven Monitoring System for the Early Pest Detection in the Precision Agriculture of Hazelnut Orchards. In 7th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL 2022 (pp.42-47) [10.1016/j.ifacol.2022.11.112].

A Data-Driven Monitoring System for the Early Pest Detection in the Precision Agriculture of Hazelnut Orchards

Lippi, Martina
;
Carpio, Renzo Fabrizio;Gasparri, Andrea
2022

Abstract

Identifying pests and treating them in a timely manner is a crucial aspect of the Precision Agriculture (PA) paradigm. Driven by the needs of the H2020 European Project Pantheon, focused on precision farming in hazelnut orchards, we propose a pest management system for gall-mites, which cause severe symptoms on generative and vegetative buds. We develop a data-driven monitoring system based on You Only Look Once (YOLO) framework enabling the early detection of the pest infestation with mean average precision of 86.7% on a holdout dataset. We perform a thorough analysis on its performance using several data augmentation methods as well as validate its real-time computation capability on a NVIDIA Jetson Xavier, which can be easily integrated into any robotic platform. Finally, we contextualize the role of the proposed detection framework in a comprehensive pest management system.
Lippi, M., Carpio, R.F., Contarini, M., Speranza, S., Gasparri, A. (2022). A Data-Driven Monitoring System for the Early Pest Detection in the Precision Agriculture of Hazelnut Orchards. In 7th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL 2022 (pp.42-47) [10.1016/j.ifacol.2022.11.112].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/423274
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