Enhancing Cocoa Pod Disease Classification via Transfer Learning and Ensemble Methods: Toward Robust Predictive Modeling
Journal:
arXiv
Published Date:
Apr 17, 2025
Abstract
This study presents an ensemble-based approach for cocoa pod disease
classification by integrating transfer learning with three ensemble learning
strategies: Bagging, Boosting, and Stacking. Pre-trained convolutional neural
networks, including VGG16, VGG19, ResNet50, ResNet101, InceptionV3, and
Xception, were fine-tuned and employed as base learners to detect three disease
categories: Black Pod Rot, Pod Borer, and Healthy. A balanced dataset of 6,000
cocoa pod images was curated and augmented to ensure robustness against
variations in lighting, orientation, and disease severity. The performance of
each ensemble method was evaluated using accuracy, precision, recall, and
F1-score. Experimental results show that Bagging consistently achieved superior
classification performance with a test accuracy of 100%, outperforming Boosting
(97%) and Stacking (92%). The findings confirm that combining transfer learning
with ensemble techniques improves model generalization and reliability, making
it a promising direction for precision agriculture and automated crop disease
management.
