# Fruit Mapping with Shape Completion for Autonomous Crop Monitoring

arXiv: https://arxiv.org/abs/2203.15489 · HTML: https://mlsanity.com/paper/2203.15489/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2022.

## Built on by (most cited first, 8 of 8)

- [Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots](https://mlsanity.com/paper/2303.08923/) (2023)
- [NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction](https://mlsanity.com/paper/2209.15376/) (2022)
- [Immature Green Apple Detection and Sizing in Commercial Orchards using YOLOv8 and Shape Fitting Techniques](https://mlsanity.com/paper/2401.08629/) (2023)
- [Semantics-Aware Next-best-view Planning for Efficient Search and Detection of Task-relevant Plant Parts](https://mlsanity.com/paper/2306.09801/) (2023)
- [Gradient-based Local Next-best-view Planning for Improved Perception of Targeted Plant Nodes](https://mlsanity.com/paper/2311.16759/) (2023)
- [Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision](https://mlsanity.com/paper/2212.01506/) (2022)
- [High-throughput 3D shape completion of potato tubers on a harvester](https://mlsanity.com/paper/2407.21341/) (2024)
- [PointRAFT: 3D deep learning for high-throughput prediction of potato tuber weight from partial point clouds](https://mlsanity.com/paper/2512.24193/) (2025)

## Abstract (arXiv)

Autonomous crop monitoring is a difficult task due to the complex structure of plants. Occlusions from leaves can make it impossible to obtain complete views about all fruits of, e.g., pepper plants. Therefore, accurately estimating the shape and volume of fruits from partial information is crucial to enable further advanced automation tasks such as yield estimation and automated fruit picking. In this paper, we present an approach for mapping fruits on plants and estimating their shape by matching superellipsoids. Our system segments fruits in images and uses their masks to generate point clouds of the fruits. To combine sequences of acquired point clouds, we utilize a real-time 3D mapping framework and build up a fruit map based on truncated signed distance fields. We cluster fruits from this map and use optimized superellipsoids for matching to obtain accurate shape estimates. In our experiments, we show in various simulated scenarios with a robotic arm equipped with an RGB-D camera that our approach can accurately estimate fruit volumes. Additionally, we provide qualitative results of estimated fruit shapes from data recorded in a commercial glasshouse environment.