| KEYWORD |
Automatic classification of healthy / diseased plants using multispectral images
keywords DEEP NEURAL NETWORKS, IMAGE PROCESSING, MACHINE LEARNING
Reference persons LUCA ARDITO, MAURIZIO MORISIO
Research Groups GR-16 - SOFTWARE ENGINEERING GROUP - SOFTENG
Thesis type EXPERIMENTAL / DEVELOPMENT
Description 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.
Required skills Python, Java, machine learning approaches and libraries, focusing on image processing, image classification
Deadline 24/08/2023
PROPONI LA TUA CANDIDATURA