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Exploration of machine learning algorithm for smart agriculture application

keywords MACHINE LEARNING, SMART AGRICULTURE

Reference persons DANILO DEMARCHI, UMBERTO GARLANDO, MAURIZIO MARTINA

Research Groups MiNES (Micro&Nano Electronic Systems)

Thesis type EXPERIMENTAL

Description The candidate will deal with the exploration of machine learning models applied to precision agriculture. In particular, impedance and environmental data will be used to create predictive models to discriminate a plant's health status. In the first part of the work, reordering and manipulation of acquired data are requested to ease the subsequent implementation of predictive algorithms. Then, an analysis of machine learning models is requested to evaluate their effectiveness in discriminating the health status of a plant. In the end, integration of the analyzed models is requested inside an existing framework implemented in Python

Required skills Python programming, Git versioning software (appreciated)

Deadline 21/11/2023      PROPONI LA TUA CANDIDATURA