Few-Shot Learning for Early Detection of Coconut Whitefly Infestations using Low-Label Leaf Image Data in Sri Lanka

Abstract

Coconut whitefly (Aleurodicus dispersus) has emerged as a devastating pest threatening Sri Lanka's vital coconut industry, causing severe yield losses and economic hardship for over 1.5 million smallholder farmers. Early detection is critical for effective intervention, but conventional field scouting is labor-intensive and often too late to prevent widespread infestation. While deep learning offers promise for automated pest detection, its deployment is hindered by the scarcity of labeled training data. To address this challenge, we propose a novel SimCLR → FixMatch framework that combines self-supervised contrastive learning with semi-supervised fine-tuning for accurate detection of coconut whitefly infestations under extreme label scarcity. Our method leverages a new dataset, CocoInfest-SL, comprising 1,600 real-world leaf images from major coconut-growing regions in Sri Lanka, with only 120 labeled samples (30 per class), 7.5% of the full dataset. The model first learns robust feature representations via SimCLR pretraining on both labeled and unlabeled data, then fine-tunes using FixMatch with confidence-based pseudo-labeling. Experimental results demonstrate that our approach achieves 91.8% accuracy and a 0.91 F1-score, outperforming state-of-the-art few-shot and semi-supervised baselines while using minimal annotations. This work presents a scalable, low-cost solution for early pest monitoring in resource-constrained agricultural environments.