3D plant phenotyping is notoriously known to be procedure-complicated and of low throughput due to the extensive multi-view imaging, the fragile 3D reconstruction pipeline, and the additional cost from reconstructed geometry to phenotypic extraction. These limitations are further amplified in low-cost data acquisition, where smartphone videos or sparsely sampled multi-view images provide limited view overlap and self-occlusion.
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...
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.
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.
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.
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.
Commercial greenhouse cucumber production is graded by fruit length, which drives harvest scheduling, labour allocation, and logistics. Manual measurement with thread or caliper is accurate but infeasible at commercial scale.
arXiv:2508.03077v2 Announce Type: replace Abstract: Feedforward 3D Gaussian Splatting (3DGS) overcomes the limitations of optimization-based 3DGS by enabling fast and high-quality reconstruction with...
Maize kernel traits such as row number, kernels per row, and kernel size vary largely for genetic reasons and are consistently associated with regions of the genome that influence yield. Manual measur...
PlantC2USeg is a deep transfer‑learning framework that uses cross‑scale consistency learning and an information‑restricted decoder to improve plant point cloud segmentation. It achieves state‑of‑the‑art performance on Soybean3D and ShapeNet Part, and demonstrates strong few‑shot generalization across species and sensing conditions. The method reduces the need for large annotated datasets and lowers adaptation overhead for new plant species.
PePESeg3D introduces perception priors into a multi‑scale 3D Gaussian segmentation pipeline, integrating monocular depth and mask constraints during geometry reconstruction and dense depth‑color cues with view‑consistent centroid supervision during contrastive feature learning. This dual‑stage approach aligns geometry with semantic structure and compensates for incomplete mask supervision from 2D foundation models. Experiments on SPIn‑NeRF, LERF‑Mask, and NVOS benchmarks show state‑of‑the‑art performance in both multi‑scale segmentation and scene reconstruction.
GaussianDS introduces a depth‑supervised framework for 3D Gaussian Splatting that jointly optimizes RGB appearance, depth, and compact semantics from scratch. By arranging multi‑view images into a pose‑aware pseudo‑video and propagating view‑consistent masks via SAM2, the method aligns semantic lifting with geometric cues, using depth supervision and edge‑aware refinement to curb semantic drift and boundary leakage. The approach achieves state‑of‑the‑art performance on LERF and 3D‑OVS benchmarks while preserving high‑fidelity reconstruction and enabling downstream tasks such as 3D object removal.