Automatic classification of healthy / diseased plants using multispectral images
Parole chiave DEEP NEURAL NETWORKS, IMAGE PROCESSING, MACHINE LEARNING
Riferimenti LUCA ARDITO, MAURIZIO MORISIO
Gruppi di ricerca GR-16 - SOFTWARE ENGINEERING GROUP - SOFTENG
Tipo tesi SPERIMENTALE APPLICATA
Descrizione The thesis is part of a research project aiming at using images collected with drones to monitor the status of plants (notably hazelnut trees).
The drone collects images (optical and infrared spectrums) of plants in regular intervals (weeks), The images are analyzed to classify each plant as healthy or non healthy.
In a second phase non healthy plants should be further classified in subclasses (water stress, insect attack, virus attack, etc).
The project team includes an agricultural company, that provides the plants, and a CNR research institute that provides botanical and agronomic know how.
The student will receive a dataset of tagged images, and will focus on finding and tuning machine learning algorithms to classify plants with the highest accuracy.
Conoscenze richieste Python, Java, machine learning approaches and libraries, focusing on image processing, image classification
Scadenza validita proposta 24/08/2023 PROPONI LA TUA CANDIDATURA