Year: 2025
Type: article
Abstract: Coffee leaf rust is a major threat to global coffee production, causing significant yield and quality losses. Traditional detection methods rely on manual inspection, which is labor-intensive, time-consuming, and susceptible to human error. This study presents an automated system for detecting coffee leaf rust using image processing techniques, combining Otsu's thresholding method with mathematical morphology to accurately identify rust-affected areas in digital images. The methodology addresses challenges such as variability in image quality, noise, and segmentation accuracy. Using a dataset of manually annotated images, the system's performance was evaluated with the Dice coefficient, demonstrating its effectiveness. Finally, we compare the results with a method that uses genetic algorithms to achieve the same goal, obtaining good results despite using a much simpler process.
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
Awards: G2441080320, G29316538
OpenAlex ID: W4407773151