Automated Detection and Cropping of Hyphae in Microscopic Images of Various Fungi

• Main Authors: Cajucom, Luis Gabriel A, Abigan, Erinn Giannice T, Ong, Josh Daniel L, Abu, Patricia Angela R, Estuar, Ma. Regina Justina E
• Format: text
• Published: Archīum Ateneo 2021
• Subjects: Fungi; Heating systems; Training; Analytical models; Microscopy; Image edge detection; Fluorescence; Microscopy image analysis; convolutional neural networks; hyphae detection; automated cropping; Computer Sciences; Databases and Information Systems; Microbiology
• Online Access: https://archium.ateneo.edu/discs-faculty-pubs/245; https://ieeexplore.ieee.org/document/9591115
• Institution: Ateneo De Manila University

Fusarium oxysporum f. sp. cubense is a soil-borne fungi that has become a major threat to the current banana industry. The presence of this fungi can destroy entire plantations if not detected and stopped early enough. The purpose of this study is to create a Convolutional Neural Network (CNN) that...