arXiv:2603.27519v4 Announce Type: replace
Abstract: Image-based plant phenotyping depends on dense structural understanding of crops, yet pixel-level annotation remains expensive across species, orga...
By Shuai Xiang, James Burridge, Shouyang Liu, Hao Lu, Tokihiro Fukatsu, Yinqiang Zheng, Wei Guo
arXiv:2608. 06404v1 Announce Type: cross Abstract: Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response.
By Junxiong Zhou, Xuechen Li, Chonghao Qiu, Lang Qiao, Xiaowei Jia, Qi Yang, Chishan Zhang, Leikun Yin, Nanshan You, Vipin Kumar, David Mulla, Ce Yang, Zhenong Jin, Licheng Liu
The paper evaluates seven 3D mesh reconstruction pipelines for crop phenotyping, assessing their fidelity and consistency both qualitatively and quantitatively. Results indicate that the GGGS, PGSR, and 2DGS pipelines produce the most accurate and visually pleasing meshes, with GGGS outperforming the next best (2DGS) by about 27% across five metrics: User ratings, Chamfer distance, LPIPS, PSNR, and SSIM.
By Karanvir Singh, Theo Morales, Binh-Son Hua, Mukesh Saini
The paper introduces SynthCrop4D, a synthetic dataset of temporally evolving plant point clouds that includes controllable noise, occlusion, and complete geometry for benchmarking reconstruction methods. It proposes a two‑stage pipeline combining spatial denoising with an Adaptive Temporal PoinTr model to recover missing regions from self‑occlusion, achieving significant improvements in reconstruction quality on both SynthCrop4D and the real Pheno4D dataset. The completed point clouds are further used to extract phenotypic traits such as plant height, canopy width, and convex hull volume, demonstrating the pipeline’s utility for high‑throughput crop phenotyping.
By Mrudul Mittal, Soumyashree Kar
The paper introduces AGS-PlantSeg, a few‑shot 3D plant organ segmentation method that uses the frozen Utonia foundation model and Adaptive Granularity Selection (AGS) to dynamically choose optimal spatial granularity for each plant. By extracting tailored geometric features for a lightweight MLP head, AGS-PlantSeg achieves superior cross‑species generalization, reaching an average mIoU of 88.9% and outperforming fixed‑granularity baselines by 2.5 points across PLANesT‑3D, Pheno4D, and Crops3D datasets. The approach requires minimal annotated data yet competes with fully supervised, plant‑specific architectures.
Plant root phenotyping is fundamental to understanding below-ground structures, optimizing crop management, and improving agricultural sustainability. This paper presents a multimodal robotic AI framework that integrates 3D skeleton extraction with language-guided reasoning for interpretable and data-efficient root analysis.