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.
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
AgriCountDINO is a parameter‑efficient, exemplar‑guided framework that jointly counts and localizes plants and their organs by conditioning frozen multiscale DINOv3 features on exemplar appearance and size, then decoding them into target points. It introduces missed‑object recovery and exemplar‑adaptive point NMS to improve detection accuracy. With only 8.4 M trainable parameters, it achieves a three‑shot MAE of 11.92 on the TPC‑268 benchmark and a zero‑shot MAE of 14.25 on unseen generic object categories in FSC‑147, outperforming previous methods without target‑domain training.
By Shengjie Guo, Xin Li, Borjana Arsova, Hanno Scharr, Silvio Salvi
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
arXiv:2607. 21881v1 Announce Type: cross Abstract: Agricultural field maps are often proprietary, incomplete, or outdated, yet they provide the spatial framework for crop monitoring, production accounting, and land-conversion analysis.
By Mohammadreza Narimani, Vikram Anand, Parastoo Farajpoor
arXiv:2409.13568v3 Announce Type: replace
Abstract: Accurate delineation of agricultural field boundaries is essential for effective crop monitoring and resource management. However, competing method...
By Foivos I. Diakogiannis, Zheng-Shu Zhou, Jeff Wang, Gonzalo Mata, Dave Henry, Roger Lawes, Amy Parker, Peter Caccetta, Suzanne Furby, Rodrigo Ibata, Ondrej Hlinka, Jonathan Richetti, Kathryn Batchelor, Chris Herrmann, Andrew Toovey, John Taylor
arXiv:2606. 14562v1 Announce Type: cross Abstract: Sociable weaver nests function as complex ecological structures offering thermoregulatory microhabitats and sustaining diverse species; however, datasets used in prior studies lack fine-grained 3D structural detail.
By Constanza A. Molina Catricheo, Simon Boeder, Ting-Jia Guo, Giacomo May, Cl\'ement Berthelot, Devis Tuia, Friedrich Fedor Reinhard, Fabio Remondino, Benjamin Risse
The paper presents the first real‑world 6D pose ground‑truth dataset for red‑stage strawberries, collected from 12,040 images at an actual farm using indirect camera pose recovery and 3D bounding‑box annotation. It also introduces a synthetic dataset rendered in NVIDIA Isaac Sim with scene‑level realism and domain randomization. Experiments show that models trained solely on synthetic data do not transfer well to in‑field images, but adding a small amount of real data significantly improves both translation and rotation accuracy across various backbone encoders.
By Woojung Son (Department of Agricultural and Biological Engineering, University of Florida), Won Suk Lee (Department of Agricultural and Biological Engineering, University of Florida), Zijing Huang (Department of Agricultural and Biological Engineering, University of Florida), Daeun Choi (Department of Agricultural and Biological Engineering, University of Florida), Catia Silva (Department of Electrical and Computer Engineering, University of Florida), Yu She (Edwardson School of Industrial Engineering, Purdue University), Yan Gu (School of Mechanical Engineering, Purdue University)
arXiv:2608.21254v1 Announce Type: cross
Abstract: Accurate agricultural weed detection in real-world field conditions is essential for precision agriculture, enabling targeted intervention and reduci...
By Nikhilesh Prabhakar, Pranuthi Tenali, Wilfredo Abudeye Fernandez, Shekhar Borah, Athresh Karanam, Erik Blasch, Prabha Sundaravadivel, Sriraam Natarajan
AgroBench is a reproducible benchmark that converts U.S. county-level crop yield statistics into weakly supervised pixel‑level crop time series. The data generation pipeline fuses USDA yield data with land cover masks, Sentinel‑2 and Sentinel‑1 imagery, climatic variables, and terrain information to produce multimodal sequences for individual crop pixels across the growing season. The benchmark includes over 13 million observations from 788,654 crop pixels, covering 5,107 county‑year combinations for five major U.S. crops from 2017 to 2024, and establishes a Leave‑One‑Year‑Out evaluation protocol with baseline machine learning results.
By Udaiveer Singh, Rajiv Ranjan, Shashank Tamaskar, Dharmendra Saraswat
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:2603. 14342v2 Announce Type: replace-cross Abstract: Modern agricultural data is sourced from diverse platforms and spans multiple spatial scales, ranging from ground-level close-up photography to Unmanned Aerial Vehicle (UAV) aerial observation and satellite remote sensing imagery.
By Jiarui Zhang, Junqi Hu, Zurong Mai, Yang Liu, Yuhang Chen, Shuohong Lou, Henglian Huang, Hong Cheng, Lingyuan Zhao, Jianxi Huang, Yutong Lu, Haohuan Fu, Juepeng Zheng