Deep Learning for In-Field Occlusion Handling and Real-Time Fruit Detection Under Dense Canopy Conditions
Robotic fruit harvesting in dense canopies remains challenging due to occlusion, variable illumination, and fruit-foliage similarity. This review synthesises recent deep learning-based detection systems, with particular focus on occlusion mitigation through multi-stage perception pipelines. The literature reveals that attention mechanisms and multi-scale feature fusion have emerged as dominant strategies for detecting partially visible fruits under leaf cover and overlapping branches. Surveyed studies report mean Average Precision scores ranging from 85–95% on orchard imagery, with YOLOv8 and Faster R-CNN serving as common benchmarks. Real-time feasibility on embedded hardware has been demonstrated across multiple systems, though the review identifies persistent gaps, including inconsistent reporting across lighting conditions and fruit maturity stages, as well as ongoing challenges in reducing false positives from visual clutter. By categorising trade-offs between accuracy, efficiency, and robustness, this review consolidates current knowledge and highlights directions toward reliable autonomous harvesting in complex agricultural environments.