| KEYWORD |
Machine learning based stem impedance data analysis for irrigation scheduling
keywords DATA ANALYSIS, IRRIGATION AND SUSTAINABLE WATER USE, MACHINE LEARNING, ARTIFICIAL NEURAL NETWORKS, SMART AGRICULTURE
Reference persons DANILO DEMARCHI, UMBERTO GARLANDO
External reference persons federico.um@polito.it
Research Groups VLSILAB (VLSI theory, design and applications)
Thesis type EXPERIMENTAL
Description The increase in population and global warming are putting the global agricultural system under strain, with the growing demand for production on one hand and the need to optimize resources, such as water, on the other. It is essential to promote the development of new technologies for real-time monitoring of plant health to anticipate their needs and maximize yields. This context forms the core of this thesis, which will focus on analyzing data related to the electrical impedance of plant stems, an innovative technique capable of providing crucial information about the health status of the plants themselves. The primary objective is the development of predictive models aimed at rapidly assessing the health status of plants and implementing timely intervention strategies and efficient irrigation scheduling.
Required skills Python Programming, Base Statistics Notions, Data Analysis
Deadline 06/03/2025
PROPONI LA TUA CANDIDATURA