Supervisor
Vikas Tomer
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
This project investigates the impact of classification methodology selection on the performance of four Convolutional Neural Network (CNN) models applied to a multi-label image dataset. The dataset consists of plant leaf images with one or more diseases. Two classification methodologies—multi-label and multi-class—are compared based on their model performance metrics. It was hypothesised that multi-label classification would perform better, but the results show that although multi-label models performed better for Loss and Accuracy metrics, they underperformed in terms of the F1 score, which is considered a more appropriate metric for this task. This surprising result refutes the initial hypothesis. Transfer learning was used to train the models, with hyperparameter tuning applied to the best-performing model. The research contributes to understanding multi-label classification and its potential in plant disease detection using machine learning.
Date of Award
2024
Full Publication Date
2024
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
O’Donohoe, Ruairi, "Comparison of Classification Methodologies using Convolutional Neural Networks in a Dataset of Plant Leaf Diseases." (2024). ICT. 58.
https://arc.cct.ie/ict/58