# Attention-driven Next-best-view Planning for Efficient Reconstruction of Plants and Targeted Plant Parts

arXiv: https://arxiv.org/abs/2206.10274 · HTML: https://mlsanity.com/paper/2206.10274/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 21 Jun 2022.

## Builds on (most cited first, 4 of 4)

- [Next-Best View Policy for 3D Reconstruction](https://mlsanity.com/paper/2008.12664/) (2020)
- [Supervised Learning of the Next-Best-View for 3D Object Reconstruction](https://mlsanity.com/paper/1905.05833/) (2019)
- [A Reinforcement Learning Approach to the View Planning Problem](https://mlsanity.com/paper/1610.06204/) (2016)
- [Viewpoint Planning for Fruit Size and Position Estimation](https://mlsanity.com/paper/2011.00275/) (2020)

## Built on by (most cited first, 10 of 10)

- [NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction](https://mlsanity.com/paper/2209.15376/) (2022)
- [Semantics-Aware Next-best-view Planning for Efficient Search and Detection of Task-relevant Plant Parts](https://mlsanity.com/paper/2306.09801/) (2023)
- [Detecting Olives with Synthetic or Real Data? Olive the Above](https://mlsanity.com/paper/2308.08271/) (2023)
- [One For All: LLM-based Heterogeneous Mission Planning in Precision Agriculture](https://mlsanity.com/paper/2506.10106/) (2025)
- [Real-World Reinforcement Learning of Active Perception Behaviors](https://mlsanity.com/paper/2512.01188/) (2025)
- [Development and evaluation of automated localisation and reconstruction of all fruits on tomato plants in a greenhouse based on multi-view perception and 3D multi-object tracking](https://mlsanity.com/paper/2211.02760/) (2022)
- [3D Pose Estimation of Tomato Peduncle Nodes using Deep Keypoint Detection and Point Cloud](https://mlsanity.com/paper/2311.04699/) (2023)
- [SG-DOR: Learning Scene Graphs with Direction-Conditioned Occlusion Reasoning for Pepper Plants](https://mlsanity.com/paper/2603.06512/) (2026)
- [Review and Evaluation of Point-Cloud based Leaf Surface Reconstruction Methods for Agricultural Applications](https://mlsanity.com/paper/2604.03328/) (2026)
- [ALFRED: Requirement-driven development of an open-source mobile manipulator for long-term plant monitoring](https://mlsanity.com/paper/2610.01477/) (2026)

## Abstract (arXiv)

Robots in tomato greenhouses need to perceive the plant and plant parts accurately to automate monitoring, harvesting, and de-leafing tasks. Existing perception systems struggle with the high levels of occlusion in plants and often result in poor perception accuracy. One reason for this is because they use fixed cameras or predefined camera movements. Next-best-view (NBV) planning presents an alternate approach, in which the camera viewpoints are reasoned and strategically planned such that the perception accuracy is improved. However, existing NBV-planning algorithms are agnostic to the task-at-hand and give equal importance to all the plant parts. This strategy is inefficient for greenhouse tasks that require targeted perception of specific plant parts, such as the perception of leaf nodes for de-leafing. To improve targeted perception in complex greenhouse environments, NBV planning algorithms need an attention mechanism to focus on the task-relevant plant parts. In this paper, the role of attention in improving targeted perception using an attention-driven NBV planning strategy was investigated. Through simulation experiments using plants with high levels of occlusion and structural complexity, it was shown that focusing attention on task-relevant plant parts can significantly improve the speed and accuracy of 3D reconstruction. Further, with real-world experiments, it was shown that these benefits extend to complex greenhouse conditions with natural variation and occlusion, natural illumination, sensor noise, and uncertainty in camera poses. The results clearly indicate that using attention-driven NBV planning in greenhouses can significantly improve the efficiency of perception and enhance the performance of robotic systems in greenhouse crop production.