Intelligent Vision and Sensing (IVS) Lab at SUNY Binghamton（SUNY布林茅伊大学智能视觉与感知实验室）；Yancheng Institute of Technology（盐城职业技术学院）；University of Georgia（佐治亚大学）
Jiakai Lin, Jinchang Zhang, Ge Jin, Wenzhan Song, Tianming Liu, Guoyu Lu
Plant roots typically exhibit a highly complex and dense architecture, incorporating numerous slender lateral roots and branches, which significantly hinders the precise capture and modeling of the entire root system. Additionally, roots often lack sufficient texture and color information, making it difficult to identify and track root traits using visual methods. Previous research on roots has been largely confined to 2D studies; however, exploring the 3D architecture of roots is crucial in botany. Since roots grow in real 3D space, 3D phenotypic information is more critical for studying genetic traits and their impact on root development. We have introduced a 3D root skeleton extraction method that efficiently derives the 3D architecture of plant roots from a few images. This method includes the detection and matching of lateral roots, triangulation to extract the skeletal structure of lateral roots, and the integration of lateral and primary roots. We developed a highly complex root dataset and tested our method on it. The extracted 3D root skeletons showed considerable similarity to the ground truth, validating the effectiveness of the model. This method can play a significant role in automated breeding robots. Through precise 3D root structure analysis, breeding robots can better identify plant phenotypic traits, especially root structure and growth patterns, helping practitioners select seeds with superior root systems. This automated approach not only improves breeding efficiency but also reduces manual intervention, making the breeding process more intelligent and efficient, thus advancing modern agriculture.
PlantPose: Universal Plant Skeleton Estimation via Tree-constrained Graph Generation
Graduate School of Information Science and Technology, Osaka University（大阪大学信息科学与技术研究生院）；Phytometrics（Phytometrics公司）；Institute of Transformative Bio-Molecules, Nagoya University（名古屋大学变革生物分子研究所）
Overcoming Small Minirhizotron Datasets Using Transfer Learning
University of Florida（佛罗里达大学）；Mississippi State University（密西西比州立大学）；University of Missouri（密苏里大学）；Argonne National Laboratory（阿贡国家实验室）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study
University of Florida（佛罗里达大学）；UniLaSalle Bauvais（UniLaSalle 博韦）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Missouri（密苏里大学）；University of Maine（缅因大学）；Argonne National Laboratory（阿贡国家实验室）
植物寄生线虫在全球范围内对农作物造成危害。图像数据的鲁棒检测是监测此类线虫的先决条件，也是许多涉及线虫C的生物学研究的先决条件。一种常见的模式生物在这里，我们提出了一个框架，用于检测蠕虫状物体的显微图像，是基于卷积神经网络（CNN）。我们用沿着身体的曲线来注释线虫，这比边界框更适合蠕虫形状的对象。经过训练的模型预测蠕虫骨架和身体端点。端点用于解开骨架，通过估计沿骨架的每个位置处的身体宽度来从骨架重建分割掩模。在轻量级骨干网络上，我们在马铃薯胞囊线虫数据集上获得了75.85%的准确率和73.02%的召回率，在公共C语言上获得了84.20%的准确率和85.63%的召回率。Elegans data set.
Accurate Tracking of Arabidopsis Root Cortex Cell Nuclei in 3D Time-Lapse Microscopy Images Based on Genetic Algorithm
Graduate School of Science and Technology, Nara Institute of Science and Technology（奈良科学技术大学研究生院）；Bioresource Engineering Laboratory, Ishikawa Prefectural University（石川县立大学生物资源工程实验室）；Department of Engineering Informatics, Osaka Electro-Communication University（大阪电讯大学工程信息学系）；Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences（国家自然科学研究院生命与生活系统探索研究中心）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Arabidopsis roots segmentation based on morphological operations and CRFs
Pladema Institute, UNCPBA（普拉德马研究所，布宜诺斯艾利斯国立大学）；Consejo Nacional de Investigaciones Científicas y Técnicas, CONICET（国家科学技术研究委员会）；Center for Visual Computing, École Centrale Paris（视觉计算中心，巴黎中央理工学校）；CNRS（法国国家科学研究中心）；Institut des Sciences du Végétal（植物科学研究所）；Université Paris Diderot（巴黎第七大学）
Stem-leaf segmentation and phenotypic trait extraction of maize shoots from three-dimensional point cloud
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）
ChronoRoot 2.0: An Open AI-Powered Platform for 2D Temporal Plant Phenotyping
Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE), CONICET-Universidad de Buenos Aires（生理学、生物分子与神经科学研究所（IFIBYNE），CONICET-布宜诺斯艾利斯大学）；APOLO Biotech（APOLO生物科技）；Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional sinc(i)（信号、系统与计算智能研究所）；CONICET-Universidad Nacional del Litoral（CONICET-沿海大学）；Université Paris-Saclay, CNRS, INRAE, Université Evry, Institute of Plant Sciences Paris-Saclay (IPS2)（巴黎萨克雷大学，CNRS，INRAE，埃夫里大学，巴黎萨克雷植物科学研究所）；Université Paris Cité, CNRS, INRAE, Institute of Plant Sciences Paris-Saclay (IPS2)（巴黎城市大学，CNRS，INRAE，巴黎萨克雷植物科学研究所）
Georeferencing of Photovoltaic Modules from Aerial Infrared Videos using Structure-from-Motion
Forschungszentrum Jülich GmbH（尤利希研究中心）；Helmholtz-Institute Erlangen-Nuremberg for Renewable Energies (HI ERN)（亥姆霍兹埃尔兰根-纽伦堡可再生能源研究所（HI ERN））；Institute Materials for Electronics and Energy Technology（电子与能源技术材料研究所）；Universität Erlangen-Nürnberg (FAU)（埃尔兰根-纽伦堡大学（FAU））
Data-driven Prediction of Species-Specific Plant Responses to Spectral-Shifting Films from Leaf Phenotypic and Photosynthetic Traits
Smart Farm Research Center, Korea Institute of Science and Technology（韩国科学技术院智能农场研究中心）；Research Institute of Agriculture and Life Sciences, Seoul National University（首尔国立大学农业与生命科学研究院）；Department of Agriculture, Forestry and Bioresources, Seoul National University（首尔国立大学农业、林业与生物资源系）
