Ensemble Convolutional Neural Networks for the Detection of Microscopic Fusarium Oxysporum

• Main Authors: Ong, Josh Daniel L, Abigan, Erinn Giannice T, Cajucom, Luis Gabriel A, Abu, Patricia Angela R, Estuar, Ma. Regina Justina E
• Format: text
• Published: Archīum Ateneo 2020
• Subjects: Fusarium oxysporum f. sp. cubense; Model stacking; Ensemble learning; Convolutional neural networks; Computer Sciences; Databases and Information Systems; Fungi; Plant Sciences
• Online Access: https://archium.ateneo.edu/discs-faculty-pubs/311; https://link.springer.com/chapter/10.1007/978-3-030-64556-4_25
• Institution: Ateneo De Manila University

The Panama disease has been reported to wipe out banana plantations due to the fungal pathogen known as Fusarium oxysporum f. sp. Cubense Tropical Race 4, or Foc TR4. Currently, there are no proven methods to control the spread of the disease. This study aims to develop an early detection model for Foc TR4 to minimize damages to infected plantations. In line with this, CNN models using the ResNet50 architecture were utilized towards the classification of the presence of Foc TR4 in a given microscopy image of a soil sample. Fungi samples were lab-cultivated, and images were taken using a lab microscope with three distinct microscopy configurations in LPO magnification. The initial results have shown that brightfield and darkfield images are generally more helpful in the automatic classification of fungi. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to validate the decision processes of the individual CNN models. The proposed ensemble model shows promising results that achieved an accuracy of 91.46%. The model is beneficial as a low-cost preliminary test that could be performed on areas that are suspected to be infected with the pathogen given that the exported models can easily be implemented in a mobile system.