Baculo, Maria Jeseca C.Rivera, Nema Rose D.Cuison, Floribeth P.2026-04-142026-04-1426-08-2024Baculo, M. J. C., Rivera, N. R. D., & Cuison, F. P. (2024). Image-based Mangifera indica pathogen recognition using artificial intelligence. Association for Computing Machinery (ACM). 157-161. https://doi.org/10.1145/3674558.36745https://doi.org/10.1145/3674558.3674580https://lakasa.dmmmsu.edu.ph/handle/123456789/1300Mangifera Indica, holds significant global export value. This study focuses on implementing object detection frameworks to identify five surface defects in this mango variety, which is crucial for maintaining its export quality. The methodology involves training four object detection frameworks. Results show that the modified region extraction technique, which uses adaptive binarization and morphological operations catered to detect mango surface defects, with EfficientNet as the base learner, demonstrated improved accuracy with a mean Average Precision (mAP) of 0.842 at an Intersection over the Union (IoU) threshold of 0.75.Mangifera indicaMango diseasesPlant pathogen detectionImage-based recognitionArtificial intelligenceComputer visionPrecision agriculturePlant injuries, diseases, and pestsImage recognition (AI aspect)Agricultural technologyMango--Diseases and pestsPlant diseases--IdentificationComputer visionArtificial intelligenceImage processing--Digital techniquesPrecision agricultureArticle