Title

Ensemble Convolutional Neural Networks for the Detection of Fusarium oxysporum f. sp. cubense in Soil Samples

Date of Award

2021

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Information Systems & Computer Science

First Advisor

Patricia Angela R. Abu, PhD

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 did not contain any soil or dirt. Images were taken using a lab microscope with three distinct microscopy techniques in LPO magnification. The brightfield model was the best performing individual CNN model in this study. Regardless, all three of the individual models were vital in the predictions of the ensemble models. Gradient-weighted Class Activation Mapping (Grad- CAM) was used to validate the decision processes of the individual CNN models. The ensemble model, though achieving an accuracy of 97.55% in this experiment, is not a sure method in determining the presence of Foc. 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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