Abstract
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.
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Acknowledgments
The authors would like to acknowledge the Ateneo Center for Computing Competency and Research (ACCCRe), the Philippine-California Advanced Research Institutes - Cloud-based Intelligent Total Analysis System (PCARI-CITAS) Project, the Commission on Higher Education (CHED), and the Department of Science and Technology - Science Education Institute (DOST-SEI) for supporting this research.
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Ong, J.D.L., Abigan, E.G.T., Cajucom, L.G., Abu, P.A.R., Estuar, M.R.J.E. (2020). Ensemble Convolutional Neural Networks for the Detection of Microscopic Fusarium Oxysporum. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12509. Springer, Cham. https://doi.org/10.1007/978-3-030-64556-4_25
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