TY - CHAP
T1 - Technological Innovations in Agriculture for Scouting Halyomorpha Halys in Orchards
AU - Almstedt, Lennart
AU - Baltieri, Davide
AU - Sorbelli, Francesco Betti
AU - Cattozzi, Davide
AU - Giannetti, Daniele
AU - Kargar, Amin
AU - Maistrello, Lara
AU - Navarra, Alfredo
AU - Niederprum, David
AU - O'Flynn, Brendan
AU - Palazzetti, Lorenzo
AU - Patelli, Niccolo
AU - Piccinini, Luca
AU - Pinotti, Cristina M.
AU - Wolf, Lars
AU - Zorbas, Dimitrios
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we illustrate the technological innovations we implemented in a test-bed field to automate the bug scouting process. Our work is motivated by the invasive global pest Halyomorpha halys (HH), whose damages have a huge economic impact for fruit orchards. We propose the automation of the time- and labor-intensive process of the HH scouting, traditionally performed by phytosanitary operators. We then describe the selection criteria that led to the hardware architecture designed consisting of a UAV, an RGB vision chip, a new ad hoc trap, and micro-climate stations. We also look for recognition algorithms based on deep learning models that can learn to recognize the HH after a training based on a dataset of images. Our very preliminary results show that the performances of UAV deep learning algorithms trained on artificial datasets are not satisfactory when tested on real images. However, very satisfactory results were obtained from the stationary ad hoc trap monitoring system running on the edge.
AB - In this paper, we illustrate the technological innovations we implemented in a test-bed field to automate the bug scouting process. Our work is motivated by the invasive global pest Halyomorpha halys (HH), whose damages have a huge economic impact for fruit orchards. We propose the automation of the time- and labor-intensive process of the HH scouting, traditionally performed by phytosanitary operators. We then describe the selection criteria that led to the hardware architecture designed consisting of a UAV, an RGB vision chip, a new ad hoc trap, and micro-climate stations. We also look for recognition algorithms based on deep learning models that can learn to recognize the HH after a training based on a dataset of images. Our very preliminary results show that the performances of UAV deep learning algorithms trained on artificial datasets are not satisfactory when tested on real images. However, very satisfactory results were obtained from the stationary ad hoc trap monitoring system running on the edge.
KW - Automatic monitoring
KW - Canopy's micro-climate station
KW - Computer Vision Algorithm
KW - Halyomorpha halys detection
KW - Hardware Evaluation
KW - IoT trap
KW - Technological transfer
UR - https://www.scopus.com/pages/publications/85174401617
U2 - 10.1109/DCOSS-IoT58021.2023.00110
DO - 10.1109/DCOSS-IoT58021.2023.00110
M3 - Chapter
AN - SCOPUS:85174401617
T3 - Proceedings - 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023
SP - 702
EP - 709
BT - Proceedings - 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023
Y2 - 19 June 2023 through 21 June 2023
ER -