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Coffee leaf disease identification and severity estimation using deep learning methods
Thesis Abstract:
This study addresses the challenges faced by the global coffee industry due to the increasing prevalence of coffee plant diseases, which affect the quality and quantity of coffee yield. This research introduces an approach integrating computer vision technology with deep learning models to detect and classify coffee diseases and estimate disease severity. A dataset of 1,086 images from various sources, including Arabica and Robusta coffee leaf images was used. These images, augmented with processing techniques, serve as the foundation for training and evaluating deep learning model, YOLO. The YOLO deep learning model classifies disease types with an overall mAP50 of 94.2%. Additionally, the model quantifies disease severity with an overall Precision of 69.6% with a confidence threshold of 0.1, enabling a comprehensive assessment of the infection in coffee plants. This dual-tier classification system empowers farmers and specialists to make informed decisions in detecting, classifying, and estimating the severity of coffee leaf diseases through YOLOv8, achieving an overall accuracy of 78.55%.