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Automated extraction of plant morphological traits is crucial for supporting crop breeding and agricultural management through high-throughput field phenotyping (HTFP). Solutions based on multi-view RGB images are attractive due to their scalability and affordability, enabling volumetric measurements that 2D approaches cannot directly capture. While advanced methods like Neural Radiance Fields (NeRFs) have shown promise, their application has been limited to counting or extracting traits from only a few plants or organs. Furthermore, accurately measuring complex structures like individual wheat heads-essential for studying crop yields-remains particularly challenging due to occlusions and the dense arrangement of crop canopies in field conditions. The recent development of 3D Gaussian Splatting (3DGS) offers a promising alternative for HTFP due to its high-quality reconstructions and explicit point-based representation. In this paper, we present Wheat3DGS, a novel approach that leverages 3DGS and the Segment Anything Model (SAM) for precise 3D instance segmentation and morphological measurement of hundreds of wheat heads automatically, representing the first application of 3DGS to HTFP. We validate the accuracy of wheat head extraction against high-resolution laser scan data, obtaining per-instance mean absolute percentage errors of 15.1%, 18.3%, and 40.2% for length, width, and volume. We provide additional comparisons to NeRF-based approaches and traditional Muti-View Stereo (MVS), demonstrating superior results. Our approach enables rapid, non-destructive measurements of key yield-related traits at scale, with significant implications for accelerating crop breeding and improving our understanding of wheat development.
A Low-Cost Photogrammetry System for 3D Plant Modeling and Phenotyping
Department of Applied Computer Science, University of Winnipeg（应用计算机科学系，温尼伯大学）；Department of Physics, University of Winnipeg（物理系，温尼伯大学）；Department of Computer Science, University of Manitoba（计算机科学系，曼尼托巴大学）；Department of Plant Sciences, University of Saskatchewan（植物科学系，萨斯喀彻温大学）
A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond
Department of Electrical and Computer Engineering, Michigan State University（电气与计算机工程系，密歇根州立大学）；Department of Systems and Information Engineering & Link Lab, University of Virginia（系统与信息工程系及Link实验室，弗吉尼亚大学）；Department of Statistics and Probability, Michigan State University（统计与概率系，密歇根州立大学）；School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University（化学、化工与生物技术学院，南洋理工大学）；Department of Agricultural and Biological Engineering, Mississippi State University（农业与生物工程系，密西西比州立大学）；Department of Mechanical Engineering, Michigan State University（机械工程系，密歇根州立大学）
Object-Centric 3D Gaussian Splatting for Strawberry Plant Reconstruction and Phenotyping
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA（电气与计算机工程系，密歇根州立大学）；Department of Mechanical Engineering, Michigan State University, East Lansing, MI, USA（机械工程系，密歇根州立大学）；Truck Crops Branch Experiment Station, Mississippi State University, Starkville, MS, USA（卡车作物分支实验站，密苏里州立大学）；Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS, USA（农业与生物工程系，密苏里州立大学）；Tandon School of Engineering, New York University, NY, USA（坦顿工程学院，纽约大学）
Australian National University（澳大利亚国立大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；Queensland University of Technology（昆士兰科技大学）
Thin leaves, fine stems, self-occlusion, non-rigid and slowly changing structures make plants difficult for three-dimensional (3D) scanning and reconstruction -- two critical steps in automated visual phenotyping. Many current solutions such as laser scanning, structured light, and multiview stereo can struggle to acquire usable 3D models because of limitations in scanning resolution and calibration accuracy. In response, we have developed a fast, low-cost, 3D scanning platform to image plants on a rotating stage with two tilting DSLR cameras centred on the plant. This uses new methods of came…
Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model
College of Biosystems Engineering and Food Science, Zhejiang University（浙江大学生物系统工程与食品科学学院）；Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs（农业农村部光谱传感重点实验室）；College of Agriculture and Biotechnology, Zhejiang University（浙江大学农业与生物技术学院）
University of Minnesota（明尼苏达大学）；University of California-Davis（加州大学戴维斯分校）
Crop production needs to increase in a sustainable manner to meet the growing global demand for food. To identify crop varieties with high yield potential, plant scientists and breeders evaluate the performance of hundreds of lines in multiple locations over several years. To facilitate the process of selecting advanced varieties, an automated framework was developed in this study. A hyperspectral camera was mounted on an unmanned aerial vehicle to collect aerial imagery with high spatial and spectral resolution. Aerial images were captured in two consecutive growing seasons from three experim…
Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems. In plant phenotyping, data competitions have a rich history, and new outdoor field datasets have potential for new data competitions. We developed the Global Wheat Challenge as a generalization competition to see if solutions for wheat head detection from field images would work in different regions around the world. In this paper, we analyze the winning challenge solutions in terms of their robustness and the relative importance of model and data augment…
GrowSplat: Constructing Temporal Digital Twins of Plants with Gaussian Splats
The AUTOLab at UC Berkeley（加州大学伯克利分校AUTOLab）；Siemens Research Lab（西门子研究实验室）；Netherlands Plant Eco-phenotyping Centre, Wageningen University and Research（荷兰植物生态表型中心，瓦赫宁根大学与研究中心）
University of Saskatchewan（萨斯喀彻温大学）；Agriculture and Agri-food Canada（加拿大农业与农业食品部）
Lodging, the permanent bending over of food crops, leads to poor plant growth and development. Consequently, lodging results in reduced crop quality, lowers crop yield, and makes harvesting difficult. Plant breeders routinely evaluate several thousand breeding lines, and therefore, automatic lodging detection and prediction is of great value aid in selection. In this paper, we propose a deep convolutional neural network (DCNN) architecture for lodging classification using five spectral channel orthomosaic images from canola and wheat breeding trials. Also, using transfer learning, we trained 1…
Counting plant organs such as heads or tassels from outdoor imagery is a popular benchmark computer vision task in plant phenotyping, which has been previously investigated in the literature using state-of-the-art supervised deep learning techniques. However, the annotation of organs in field images is time-consuming and prone to errors. In this paper, we propose a fully unsupervised technique for counting dense objects such as plant organs. We use a convolutional network-based unsupervised segmentation method followed by two post-hoc optimization steps. The proposed technique is shown to prov…
UAV3DCrop: Benchmarking 3D Reconstruction in Repeated Multi-Angle UAV Crop Surveys
University of Minnesota, Twin Cities（明尼苏达大学双城校区）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；University of Pittsburgh（匹兹堡大学）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；Boston University（波士顿大学）；Peking University（北京大学）
Panicle density of cereal crops such as wheat and sorghum is one of the main components for plant breeders and agronomists in understanding the yield of their crops. To phenotype the panicle density effectively, researchers agree there is a significant need for computer vision-based object detection techniques. Especially in recent times, research in deep learning-based object detection shows promising results in various agricultural studies. However, training such systems usually requires a lot of bounding-box labeled data. Since crops vary by both environmental and genetic conditions, acquis…
StomaD2: An All-in-One System for Intelligent Stomatal Phenotype Analysis via Diffusion-Based Restoration Detection Network
Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms（河南人工智能理论与算法工程研究中心）；School of Mathematics and Statistics（数学与统计学学院）；International Joint Research Laboratory for Global Change Ecology（全球变化生态学联合研究实验室）；School of Life Sciences（生命科学学院）；State Key Laboratory of Cotton Biology（棉花生物学国家重点实验室）
