The accurate classification of plant organs is a key step in monitoring the growing status and physiology of plants. A classification method was proposed to classify the leaves and stems of potted plants automatically based on the point cloud data of the plants, which is a nondestructive acquisition. The leaf point training samples were automatically extracted by using the three-dimensional convex hull algorithm, while stem point training samples were extracted by using the point density of a two-dimensional projection. The two training sets were used to classify all the points into leaf points and stem points by utilizing the support vector machine (SVM) algorithm. The proposed method was tested by using the point cloud data of three potted plants and compared with two other methods, which showed that the proposed method can classify leaf and stem points accurately and efficiently.
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…
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 …
Reliable and automated 3D plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level. Combining deep learning and point clouds can provide effective ways to address the challenge. However, fully supervised deep learning methods require datasets to be point-wise annotated, which is extremely expensive and time-consuming. In our work, we proposed a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation. First, high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system, and the Mes…
Accurately phenotyping plant wilting is important for understanding responses to environmental stress. Analysis of the shape of plants can potentially be used to accurately quantify the degree of wilting. Plant shape analysis can be enhanced by locating the stem, which serves as a consistent reference point during wilting. In this paper, we show that deep learning methods can accurately segment tomato plant stems. We also propose a control-point-based ground truth method that drastically reduces the resources needed to create a training dataset for a deep learning approach. Experimental result…
PLANesT-3D: A new annotated dataset for segmentation of 3D plant point clouds
Faculty of Agriculture（农业学院）；Usak University（乌萨克大学）；Department of Electrical-Electronics Engineering（电气电子工程系）；Eskisehir Osmangazi University（埃斯基谢普大学）；SADC Department（SADC部门）；Huawei Türkiye R&D Center（华为土耳其研发中心）；Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS)（安热斯工程系统研究实验室）；Université d’Angers（安格斯大学）
Applying agrochemicals is the default procedure for conventional weed control in crop production, but has negative impacts on the environment. Robots have the potential to treat every plant in the field individually and thus can reduce the required use of such chemicals. To achieve that, robots need the ability to identify crops and weeds in the field and must additionally select effective treatments. While certain types of weed can be treated mechanically, other types need to be treated by (selective) spraying. In this paper, we present an approach that provides the necessary information for …
Robust Plant Disease Diagnosis with Few Target-Domain Samples
Department of Applied Informatics, Graduate School of Science and Engineering, Hosei University（应用信息系，理工学研究院，恒生大学）；Department of Clinical Plant Science, Faculty of Bioscience and Applied Chemistry, Hosei University（临床植物科学系，生物科学与应用化学学部，恒生大学）
High efficiency in precision farming depends on accurate tools to perform weed detection and mapping of crops. This allows for precise removal of harmful weeds with a lower amount of pesticides, as well as increase of the harvest's yield by providing the farmer with valuable information. In this paper, we address the problem of fully automatic stem detection from image data for this purpose. Our approach runs on mobile agricultural robots taking RGB images. After processing the images to obtain a vegetation mask, our approach separates each plant into its individual leaves and later estimates …
One of the critical biotic stress factors paddy farmers face is diseases caused by bacteria, fungi, and other organisms. These diseases affect plants' health severely and lead to significant crop loss. Most of these diseases can be identified by regularly observing the leaves and stems under expert supervision. In a country with vast agricultural regions and limited crop protection experts, manual identification of paddy diseases is challenging. Thus, to add a solution to this problem, it is necessary to automate the disease identification process and provide easily accessible decision support…
Unsupervised deep learning for semantic segmentation of multispectral LiDAR forest point clouds
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland（遥感与摄影测量系，芬兰地理研究 institute FGI，芬兰国家土地测绘局）；D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK)（3D光学计量（3DOM）单元，布鲁诺·凯瑟尔基金会（FBK））；Department of Built Environment, Aalto University, School of Engineering（环境学院，艾尔托大学，工程学院）
