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
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...
arXiv:2609.12350v1 Announce Type: new
Abstract: Close-range first-person field images are important for mobile maize phenotyping because many plant-level traits depend on in-canopy structures that ar...
By Jiayi Li, Zihan Zhang, Erhankang Yan, Yitian Chen, Yuze Li, Chengzhang Ding, Jianxin Cao
The paper presents a fully automated pipeline that extracts maize ear traits—such as kernel count, row number, and kernel size—from 3D point clouds generated by a video-to-point-cloud platform. The method processes raw video through COLMAP and NeRF, isolates the ear, calibrates the point cloud, aligns it, unwraps it into a 2D image, and applies Cellpose‑SAM for instance segmentation, achieving high accuracy (kernel count R² = 0.921, MAPE = 10.33 %) on a held‑out dataset. The resulting multi‑trait dataset, with genotype identities, is ready for phenotype‑to‑genotype association studies.
By Ritwesh A. Kumar, Som Tripathi, Peja Matthews, Srikar Reddy, Talukder Zaki Jubery, Patrick Schnable, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
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: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
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.
By Yu Tian, Xintong Jiang, Jan Franklin Adamowski, Shiv O. Prasher, Shangpeng Sun
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.
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
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
The paper presents a benchmark to test whether vision‑language models can produce plant simulation configurations from images using in‑context learning. It focuses on cowpea plot reconstruction, requiring the models to output structured JSON that includes field and plant details. Open‑source multimodal models from the Gemma 4 and Qwen3.5 families are evaluated on synthetic and real drone datasets, using five in‑context methods, and the results show that while VLMs can generate valid JSON and estimate key agronomic metrics, their performance varies and often lags behind dataset baselines.
By Heesup Yun, Isaac Kazuo Uyehara, Earl Ranario, Lars Lundqvist, Christine H. Diepenbrock, Brian N. Bailey, J. Mason Earles
arXiv:2607. 06948v1 Announce Type: cross Abstract: The accuracy of existing leaf-wood segmentation methods for tree point clouds varies across forest types and sites.
By Heeju Mun, Tackang Yang, Yunsoo Nam, Changhyun Choi