A Data-Driven Monitoring System for the Early Pest Detection in the Precision Agriculture of Hazelnut Orchards
Martina Lippi, Renzo Fabrizio Carpio, Mario Contarini, Stefano Speranza, Andrea Gasparri
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.