Computationally Efficient Transfer Learning Pipeline for Oil Palm Fresh Fruit Bunch Defect Detection

Abstract

The present study addresses the inefficiencies of the manual classification of oil palm fresh fruit bunches (FFBs) by introducing a computationally efficient alternative to traditional deep learning approaches that require extensive retraining and large datasets. Using feature-based transfer learning, where pre-trained Convolutional Neural Network architectures, namely EfficientNet_B0, EfficientNet_B4, ResNet152, and VGG16, serve as fixed feature extractors coupled with the Logistic Regression classifier, this research evaluated the performance on a dataset of 466 images categorized as defective or non-defective. The results demonstrate a robust classification performance across all architectures, with the EfficientNet_B4–LR pipeline achieving an exceptional accuracy value of 96.81%, which was further enhanced through hyperparameter optimization. This confirms that feature-based transfer learning offers a reliable, resource-efficient, and practical solution for automated FFB defect detection that can significantly benefit the palm oil industry by providing a scalable alternative to subjective manual-grading methods.