| KEYWORD |
Microelectronics
Real-Time Detection of Plant Parasites and Diseases in Smart Agriculture
Tesi esterna in azienda
Parole chiave IMAGE ANALYSIS, MACHINE LEARNING
Riferimenti LUCIANO LAVAGNO
Riferimenti esterni Marcello babbi, Reply Torino
Gruppi di ricerca Microelectronics
Tipo tesi APPLIED RESEARCH
Descrizione In smart agriculture, early detection and control of plant parasites and diseases are crucial for maintaining
high crop yields and minimizing economic losses. This thesis proposes the development of a cutting-edge AIbased
system for the real-time detection of plant parasites and diseases in smart agriculture. The system will
use computer vision and machine learning techniques to analyze images of plants captured by sensors and
identify patterns. The system will be designed to run on edge devices, allowing for real-time processing of
images and immediate feedback to farmers. The use of state-of-the-art AI technologies, such as deep
learning and transfer learning, will enable the system to learn and adapt to new patterns of parasites and
diseases over time, leading to more accurate and reliable detections.
Conoscenze richieste Python programming, some knowledge of Machine Learning, image processing
Note The student will be involved in:
❑ State-of-the-art literature review
❑ SW requirements definition for edge deployment
❑ Data pre-processing and feature engineering
❑ Developing and implementing a deep learning-based model for image analysis
❑ HW computational requirements trade-off
Scadenza validita proposta 11/10/2024
PROPONI LA TUA CANDIDATURA