Supervisor
Taufique Ahmed
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
This research focuses on improving the detection and classification of maize crop pests and diseases to enhance agricultural yield and food security. A dataset comprising 5389 images of maize conditions (healthy, pest-affected, and disease-affected) across seven classes was used. The images underwent preprocessing, including resizing to 299x299, class balancing using augmentation techniques, and noise reduction with Gaussian filtering.
Feature extraction utilised EfficientNetB0 and InceptionV3 architectures, with PCA employed for feature selection. Classification was conducted using a Support Vector Machine (SVM) with a One-vs-One strategy, alongside a baseline 2D CNN model. Data engineering included label encoding, standardisation, and an 80:10:10 train-test-validation split. Hyperparameter optimisation was performed via Grid Search CV for SVM and Random Search for the 2D CNN.
The best-performing model, EfficientNetB0+SVM (299x299), achieved an accuracy of 93%, outperforming other models, including the standalone 2D CNN, which reached 78%. This underscores the advantage of hybrid models over standalone CNNs in classification tasks for pest and disease detection in maize crops.
Date of Award
2024
Full Publication Date
2024
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Namaemba, Diana Flora, "Maize Crop Pests and Diseases Classification Using Hybrid Models." (2024). ICT. 64.
https://arc.cct.ie/ict/64