Fruit detection for small datasets via adjustable anchor boxes and transfer learning

Abstract

Fruit detection is a crucial task in plant phenotyping but remains challenging due to limited training data, high variability in fruit appearances across different growth stages, and occlusions that hinder accurate detection. To address these issues, we propose an Adjustable Anchor Box Detection Network with Transfer Learning (ADNet_TL) for robust fruit detection under unstructured conditions. Our approach leverages the backbone of an existing detector to extract discriminative fruit regions, integrates two adjustable anchor box mechanisms that align with dataset-specific characteristics, and employs transfer learning to boost performance on small target datasets effectively. A comprehensive analysis is conducted to assess the impact of training sample sizes in both source and target domains. Experimental evaluations on Strawberry, Tomato, and Multi-fruit datasets reveal that ADNet_TL outperforms both the standard ADNet and the classical Single Shot MultiBox Detector (SSD), with up to a 14% improvement in mean Average Precision (mAP). These results underscore the potential of ADNet_TL for practical applications such as fruit forecasting and selective harvesting in real agricultural scenarios.