Résumé:
This master’s thesis focuses on the detection of diseases on apple tree branches
using artificial intelligence (AI). The aim of this study is to develop an intelligent sys-
tem that can automatically detect and segment diseases on apple tree branches based
on image analysis. Traditional manual detection methods are time-consuming, costly,
and prone to human errors. By leveraging advancements in machine learning, image
processing, and deep learning algorithms, an AI-based system can provide accurate
and real-time information about the health status of apple trees, enabling farmers to
take preventive measures and optimize their crop production. The proposed system
combines machine learning techniques with image analysis and data processing to
assist farmers in maintaining the health of their trees and reducing crop losses. This
thesis presents the design, implementation, and evaluation of the system, demonstra-
ting its effectiveness in disease detection on apple tree branches.
