| KEYWORD |
Utilizing AI for Precision Diagnosis of Vine leaf Diseases
Thesis in external company
keywords ARTIFICIAL INTELLIGENCE, DEEP LEARNING, COMPUTER V
Reference persons ANDREA BOTTINO
External reference persons Pro-Logic, Torino
Research Groups DAUIN - GR-02 - COMPUTER GRAPHIC AND VISION GROUP - CGVG
Thesis type RESEARCH THESIS WITH A COMPANY
Description The DIVINE (DIagnosi delle malattie della VIte per immagini tramite le reti NEurali e il deep learning) project is an initiative aligned with the European Green Deal, aimed at transforming the way vine diseases are detected and treated. This thesis aims at developing Computer Vision (Depp-learning based) methodologies for automatically and accurately diagnose from images (in the visible and multispectral interval) major vine diseases, such as Downy Mildew (Peronospora) and Powdery Mildew (Oidio).
This project is a collaborative effort, bringing together entities from various sectors including enterprises, academic institutions, agronomists, and sensor technology experts. The collaborative nature of the project is aimed at leveraging a wide spectrum of expertise to effective solution and build a comprehensive, annotated dataset from both controlled experiments and real-world crop scenarios that can be used to train the devised models.
Internship opportunity in company with expense reimbursement. Thesis available for multiple students.
Further information are available at the following linK: https://areeweb.polito.it/ricerca/cgvg/thesis.html
See also https://areeweb.polito.it/ricerca/cgvg/thesis.html
Required skills Machine Learning, Deep Learning, Python
Deadline 15/01/2025
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