Accurate reconstruction of leaf surfaces from 3D point cloud is essential for agricultural applications such as phenotyping. However, real-world plant data (i.e., irregular 3D point cloud) are often complex to reconstruct plant parts accurately. A wide range of surface reconstruction methods has been proposed, including parametric, triangulation-based, implicit, and learning based approaches, yet their relative performance for leaf surface reconstruction remains insufficiently understood. In this work, we present a comparative study of nine representative surface reconstruction methods for leaf surfaces. We evaluate these methods on three publicly available datasets: LAST-STRAW, Pheno4D, and Crops3D - spanning diverse species, sensors, and sensing environments, ranging from clean high-resolution indoor scans to noisy low-resolution field settings. The analysis highlights the trade-offs between surface area estimation accuracy, smoothness, robustness to noise and missing data, and computational cost across different methods. These factors affect the cost and constraints of robotic hardware used in agricultural applications. Our results show that each method exhibits distinct advantages depending on application and resource constraints. The findings provide practical guidance for selecting surface reconstruction techniques for resource constrained robotic platforms.
DFKI - German Research Center for Artificial Intelligence（德国人工智能研究中心）
This paper demonstrates a system capable of combining a sparse, indirect, monocular visual SLAM, with both offline and real-time Multi-View Stereo (MVS) reconstruction algorithms. This combination overcomes many obstacles encountered by autonomous vehicles or robots employed in agricultural environments, such as overly repetitive patterns, need for very detailed reconstructions, and abrupt movements caused by uneven roads. Furthermore, the use of a monocular SLAM makes our system much easier to integrate with an existing device, as we do not rely on a LiDAR (which is expensive and power consum…
High-fidelity 3D Reconstruction of Plants using Neural Radiance Field
College of Engineering, South China Agriculture University（华南农业大学工程学院）；Department of Mechanical and Aerospace Engineering, Monash University（莫纳什大学机械与航空航天工程系）
College of Information Sciences and Technology, Donghua University（东华大学信息科学与技术学院）；Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education, Donghua University（东华大学数字化纺织服装技术教育部工程研究中心）
Automatic leaf segmentation, as well as identification and classification methods that built upon it, are able to provide immediate monitoring for plant growth status to guarantee the output. Although 3D plant point clouds contain abundant phenotypic features, plant leaves are usually distributed in clusters and are sometimes seriously overlapped in the canopy. Therefore, it is still a big challenge to automatically segment each individual leaf from a highly crowded plant canopy in 3D for plant phenotyping purposes. In this work, we propose an overlapping-free individual leaf segmentation meth…
Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain
University of Bonn（波恩大学）；University of Oxford（牛津大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；La Sapienza University of Rome（罗马第一大学）
We introduce a labeling tool and dataset aimed to facilitate computer vision research in agriculture. The annotation tool introduces novel methods for labeling with a variety of manual, semi-automatic, and fully-automatic tools. The dataset includes original images collected from commercial greenhouses, images from PlantVillage, and images from Google Images. Images were annotated with segmentations for foreground leaf, fruit, and stem instances, and diseased leaf area. Labels were in an extended COCO format. In total the dataset contained 10k tomatoes, 7k leaves, 2k stems, and 2k diseased lea…
Sapling-NeRF: Geo-Localised Sapling Reconstruction in Forests for Ecological Monitoring
Oxford Robotics Institute, Department of Engineering Science, University of Oxford, Oxford, UK（牛津大学机器人研究所、工程科学系、牛津大学、牛津、英国）；Group of Automation, Robotics and Computer Vision, University of Alicante, Alicante, Spain（自动化、机器人与计算机视觉小组、阿尔瓦登特大学、阿尔瓦登特、西班牙）；Department of Biology, University of Oxford, Oxford, UK（生物学系、牛津大学、牛津、英国）
College of Information and Electrical Engineering, Shenyang Agricultural University（沈阳农业大学信息与电气工程学院）；School of Information and Intelligence Engineering, University of Sanya（三亚学院信息与智能工程学院）
Nowadays, there are many approaches to acquire three-dimensional (3D) point clouds of maize plants. However, automatic stem-leaf segmentation of maize shoots from three-dimensional (3D) point clouds remains challenging, especially for new emerging leaves that are very close and wrapped together during the seedling stage. To address this issue, we propose an automatic segmentation method consisting of three main steps: skeleton extraction, coarse segmentation based on the skeleton, fine segmentation based on stem-leaf classification. The segmentation method was tested on 30 maize seedlings and …
Empowering Agricultural Insights: RiceLeafBD -- A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique
Leading University（领先大学）；Daffodil International University（水仙国际大学）；Queensborough Community College（皇后borough社区学院）
Deep learning techniques involving image processing and data analysis are constantly evolving. Many domains adapt these techniques for object segmentation, instantiation and classification. Recently, agricultural industries adopted those techniques in order to bring automation to farmers around the globe. One analysis procedure required for automatic visual inspection in this domain is leaf count and segmentation. Collecting labeled data from field crops and greenhouses is a complicated task due to the large variety of crops, growth seasons, climate changes, phenotype diversity, and more, espe…
MaizeField3D: A Curated 3D Point Cloud and Procedural Model Dataset of Field-Grown Maize from a Diversity Panel
Department of Mechanical Engineering, Iowa State University, Ames, USA（机械工程系，爱荷华州立大学）；Translational AI Research and Education Center, Ames, USA（转化人工智能研究与教育中心）；Plant Science Institute, Iowa State University, Ames, USA（植物科学研究所）；Interdepartmental Genetics and Genomics Graduate Program, Iowa State University, Ames, USA（跨部门遗传学和基因组学研究生项目）；Department of Agronomy, Iowa State University, Ames, USA（农业学系）
Iterative Motion Compensation for Canonical 3D Reconstruction from UAV Plant Images Captured in Windy Conditions
Autonomous Intelligent Systems - Computer Science Institute VI and Center for Robotics, University of Bonn, Germany（自主智能系统 - 计算科学研究所VI和机器人中心，波恩大学，德国）；Lamarr Institute for Machine Learning and Artificial Intelligence, Germany（拉玛尔机器学习与人工智能研究所，德国）
Federal University of Espírito Santo（圣埃斯皮里图联邦大学）；Incaper（因卡佩尔）
Biotic stress consists of damage to plants through other living organisms. Efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability. Agricultural sustainability promotes the development of new technologies that allow the reduction of environmental impacts, greater accessibility to farmers and, consequently, increase on productivity. The use of computer vision with deep learning methods allows the early and correct identification of the stress-causing agent. So, corrective measures can be app…
University of Bonn（波恩大学）；University of Oxford（牛津大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；La Sapienza University of Rome（罗马第一大学）
Agricultural robots have the prospect to enable more efficient and sustainable agricultural production of food, feed, and fiber. Perception of crops and weeds is a central component of agricultural robots that aim to monitor fields and assess the plants as well as their growth stage in an automatic manner. Semantic perception mostly relies on deep learning using supervised approaches, which require time and qualified workers to label fairly large amounts of data. In this paper, we look into the problem of reducing the amount of labels without compromising the final segmentation performance. Fo…
LeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping
Leibniz Institute for Agricultural Engineering（莱布尼茨农业工程与生物经济研究所）；BASF Belgium Coordination Center CommV（巴斯夫比利时协调中心）；BASF Metabolome Solutions GmbH（巴斯夫代谢组解决方案公司）；Technical University Berlin（柏林技术大学）；University of Potsdam（波茨坦大学）
An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI
Ahsanullah University of Science and Technology（阿沙努拉科学与技术大学）；Southeast University（东南大学）；Bangladesh University of Engineering and Technology（孟加拉工程与技术大学）
An efficient plant disease detection using transfer learning approach
Mohan Babu University（莫汉巴布大学）；University of Nigeria（尼日利亚大学）；Tecnologico de Monterrey（蒙特雷理工学院）；Coal City University（煤炭城大学）；Saveetha Institute of Medical and Technical Sciences（萨维塔医学与技术科学学院）
In many agricultural applications one wants to characterize physical properties of plants and use the measurements to predict, for example biomass and environmental influence. This process is known as phenotyping. Traditional collection of phenotypic information is labor-intensive and time-consuming. Use of imagery is becoming popular for phenotyping. In this paper, we present methods to estimate traits of sorghum plants from RBG cameras on board of an unmanned aerial vehicle (UAV). The position and orientation of the imagery together with the coordinates of sparse points along the area of int…
