In this paper, we present a case study on the transition to informed automated decision-making processes in smart agriculture. Our focus is on addressing the challenges posed by the new invasive global pest, Halyomorpha halys (HH), which causes significant economic damage to fruit orchards. Specifically, we aim to automate the time-and labor-intensive process of HH scouting, which is traditionally performed by phytosanitary operators. Our objective is to demonstrate the pipeline of technological and methodological decisions necessary for automating the scouting process. To gather images from the orchard, we utilized a drone equipped with an RGB camera as well as other devices such as smartphones. Despite the suboptimal quality of the images captured by the drone’s camera, our computer vision algorithm for HH detection yields promising results. These findings serve as an encouragement to further explore the possibilities of technology transfer to the agriculture.

Preliminary Results for Halyomorpha halys Monitoring Relying on a Custom Dataset / Francesco Betti Sorbelli, Lorenzo Palazzetti, Cristina M Pinotti. - ELETTRONICO. - (2023), pp. 0-0. (Intervento presentato al convegno 2023 IEEE INTERNATIONAL WORKSHOP ON Metrology for Agriculture and Forestry) [10.1109/MetroAgriFor58484.2023.10424403].

Preliminary Results for Halyomorpha halys Monitoring Relying on a Custom Dataset

Francesco Betti Sorbelli
;
Lorenzo Palazzetti;
2023

Abstract

In this paper, we present a case study on the transition to informed automated decision-making processes in smart agriculture. Our focus is on addressing the challenges posed by the new invasive global pest, Halyomorpha halys (HH), which causes significant economic damage to fruit orchards. Specifically, we aim to automate the time-and labor-intensive process of HH scouting, which is traditionally performed by phytosanitary operators. Our objective is to demonstrate the pipeline of technological and methodological decisions necessary for automating the scouting process. To gather images from the orchard, we utilized a drone equipped with an RGB camera as well as other devices such as smartphones. Despite the suboptimal quality of the images captured by the drone’s camera, our computer vision algorithm for HH detection yields promising results. These findings serve as an encouragement to further explore the possibilities of technology transfer to the agriculture.
2023
2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)
2023 IEEE INTERNATIONAL WORKSHOP ON Metrology for Agriculture and Forestry
Francesco Betti Sorbelli, Lorenzo Palazzetti, Cristina M Pinotti
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1347495
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