that incorporates crop visibility and mask consistency, enabling robustness against occlusions and annotation discrepancies. • We release a public infield cotton plant dataset designed for 3D rendering and cotton boll counting tasks. The source code, dataset, and multimedia material associated with this project can be found at https://robotic-vision-lab.github.io/cropnerf . II Related Work II-A Image-Based Techniques Image-based methods typically employ object detection to identify crops within images. For example, Chen et al. [ 4 ] utilized multiple convolutional neural networks (CNNs) to map input images to total fruit counts. Similarly, Häni et al. [ 5 ] formulated crop counting as a mult
Open resource ↗lines:108-187Unverified paper record
CropNeRF: A Neural Radiance Field-Based Framework for Crop Counting
arXiv · 1 Jan 2026 · 10.48550/arxiv.2601.00207
Abstract
Rigorous crop counting is crucial for effective agricultural management and informed intervention strategies. However, in outdoor field environments, partial occlusions combined with inherent ambiguity in distinguishing clustered crops from individual viewpoints poses an immense challenge for image-based segmentation methods. To address these problems, we introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation. Our approach utilizes 2D images captured from multiple viewpoints and associates independent instance masks for neural radiance field (NeRF) view synthesis. We introduce crop visibility and mask consistency scores, which are incorporated alongside 3D information from a NeRF model. This results in an effective segmentation of crop instances in 3D and highly-accurate crop counts. Furthermore, our method eliminates the dependence on crop-specific parameter tuning. We validate our framework on three agricultural datasets consisting of cotton bolls, apples, and pears, and demonstrate consistent counting performance despite major variations in crop color, shape, and size. A comparative analysis against the state of the art highlights superior performance on crop counting tasks. Lastly, we contribute a cotton plant dataset to advance further research on this topic.
Plant phenotyping relevance
NeRFと3Dインスタンスセグメンテーションを用いて作物個体・器官数を推定する画像ベース表現型計測手法を開発・検証しており、方法が研究の中心である。
abstractwe introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation.
abstractWe introduce crop visibility and mask consistency scores, which are incorporated alongside 3D information from a NeRF model.
abstractWe validate our framework on three agricultural datasets consisting of cotton bolls, apples, and pears
abstractLastly, we contribute a cotton plant dataset to advance further research on this topic.
Code and data availability
The paper contributes a public infield cotton plant dataset (8 plants, ~150 iPhone images each, ground-truth boll counts, SAM instance masks) and states that source code, dataset, and multimedia are available at the authors' public project page, which is an allowed URL. The spectacularai GitHub URL is a generic third-p
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