Détection des maladies des pommiers sur les branches

Abstract

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.