| KEYWORD |
Microelectronics
Real-Time Detection of Plant Parasites and Diseases in Smart Agriculture
Thesis in external company
keywords IMAGE ANALYSIS, MACHINE LEARNING
Reference persons LUCIANO LAVAGNO
External reference persons Marcello babbi, Reply Torino
Research Groups Microelectronics
Thesis type APPLIED RESEARCH
Description 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.
Required skills Python programming, some knowledge of Machine Learning, image processing
Notes 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
Deadline 11/10/2024
PROPONI LA TUA CANDIDATURA