# Kiwifruit detection in challenging conditions

arXiv: https://arxiv.org/abs/2006.11729 · HTML: https://mlsanity.com/paper/2006.11729/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Pallet Detection from Synthetic Data Using Game Engines](https://mlsanity.com/paper/2304.03602/) (2023)
- [Vision-Based Early Fault Diagnosis and Self-Recovery for Strawberry Harvesting Robots](https://mlsanity.com/paper/2601.02085/) (2026)
- [Detection of Spider Mites on Labrador Beans through Machine Learning Approaches Using Custom Datasets](https://mlsanity.com/paper/2402.07895/) (2024)

## Abstract (arXiv)

Accurate and reliable kiwifruit detection is one of the biggest challenges in developing a selective fruit harvesting robot. The vision system of an orchard robot faces difficulties such as dynamic lighting conditions and fruit occlusions. This paper presents a semantic segmentation approach with two novel image prepossessing techniques designed to detect kiwifruit under the harsh lighting conditions found in the canopy. The performance of the presented system is evaluated on a 3D real-world image set of kiwifruit under different lighting conditions (typical, glare, and overexposed). Alone the semantic segmentation approach achieves an F1_score of 0.82 on the typical lighting image set, but struggles with harsh lighting with an F1_score of 0.13. Utilising the prepossessing techniques the vision system under harsh lighting improves to an F1_score 0.42. To address the fruit occlusion challenge, the overall approach was found to be capable of detecting 87.0% of non-occluded and 30.0% of occluded kiwifruit across all lighting conditions.