Innovative 3D modelling processing methods for chestnut burr detection
Thesis in external company
keywords AGRICOLTURE 4.0, ARTIFICIAL INTELLIGENCE, IMAGE PROCESSING, IMAGE SEGMENTATION, PHOTOGRAMMETRY, POINT CLOUD, REMOTE SENSING, SMART AGRICULTURE, UAV, DRONES
Reference persons LORENZO COMBA
Thesis type SPERIMENTAL, SPERIMENTAL APPLIED
Description The proposed activity is in collaboration with DiSAFA (Department of Agricultural, Forest and Food Sciences) of UniTO, and it is within the framework of a research project VALTIFRU 4.0 (Enhancement of the supply chains of nuts and fresh processed fruits with high added value) funded by EU PNR 2015-2020. In 2021 and 2022, an extensive monitoring campaign has been conducted in four chestnut fields near Catanzaro, collecting several dataset: in-field observations, high-resolution aerial multispectral maps and 3D models (by Matrice 300 UAV and MAIA camera), and many others.
The aim of the Thesis is to develop new point cloud processing methods for chestnut burr detection and, thus, yield estimation.
Results of the proposed activity will enable the development of new decision support system (DSS) for enhanced precision agriculture methodologies, within the framework of Agriculture 4.0.
See also 01__valtifru.pdf https://www.linkedin.com/company/laboratorio-di-meccatronica-universit%C3%A0-di-torino/
Required skills Preferred (but not compulsory) computing environment: MathWorks MATLAB
Deadline 08/07/2023 PROPONI LA TUA CANDIDATURA