Plant Phenomics最新文献_第2页

Phenomic Selection for Hybrid Rapeseed Breeding. 杂交油菜籽育种的表型选择。

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-07-24 eCollection Date: 2024-01-01 DOI: 10.34133/plantphenomics.0215

Lennard Roscher-Ehrig, Sven E Weber, Amine Abbadi, Milka Malenica, Stefan Abel, Reinhard Hemker, Rod J Snowdon, Benjamin Wittkop, Andreas Stahl

{"title":"Phenomic Selection for Hybrid Rapeseed Breeding.","authors":"Lennard Roscher-Ehrig, Sven E Weber, Amine Abbadi, Milka Malenica, Stefan Abel, Reinhard Hemker, Rod J Snowdon, Benjamin Wittkop, Andreas Stahl","doi":"10.34133/plantphenomics.0215","DOIUrl":"https://doi.org/10.34133/plantphenomics.0215","url":null,"abstract":"Phenomic selection is a recent approach suggested as a low-cost, high-throughput alternative to genomic selection. Instead of using genetic markers, it employs spectral data to predict complex traits using equivalent statistical models. Phenomic selection has been shown to outperform genomic selection when using spectral data that was obtained within the same generation as the traits that were predicted. However, for hybrid breeding, the key question is whether spectral data from parental genotypes can be used to effectively predict traits in the hybrid generation. Here, we aimed to evaluate the potential of phenomic selection for hybrid rapeseed breeding. We performed predictions for various traits in a structured population of 410 test hybrids, grown in multiple environments, using near-infrared spectroscopy data obtained from harvested seeds of both the hybrids and their parental lines with different linear and nonlinear models. We found that phenomic selection within the hybrid generation outperformed genomic selection for seed yield and plant height, even when spectral data was collected at single locations, while being less affected by population structure. Furthermore, we demonstrate that phenomic prediction across generations is feasible, and selecting hybrids based on spectral data obtained from parental genotypes is competitive with genomic selection. We conclude that phenomic selection is a promising approach for rapeseed breeding that can be easily implemented without any additional costs or efforts as near-infrared spectroscopy is routinely assessed in rapeseed breeding.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11268845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141763988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating Neural Radiance Fields (NeRFs) for 3D Plant Geometry Reconstruction in Field Conditions 评估神经辐射场 (NeRF) 在田间条件下用于三维植物几何重建的效果

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-07-24 DOI: 10.34133/plantphenomics.0235

Muhammad Arbab Arshad, T. Jubery, James Afful, Anushrut Jignasu, Aditya Balu, B. Ganapathysubramanian, Soumik Sarkar, A. Krishnamurthy

引用次数: 0

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images. 利用陆地高光谱图像对水稻氮磷钾胁迫进行特征描述和识别

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-07-24 eCollection Date: 2024-01-01 DOI: 10.34133/plantphenomics.0197

Jinfeng Wang, Yuhang Chu, Guoqing Chen, Minyi Zhao, Jizhuang Wu, Ritao Qu, Zhentao Wang

{"title":"Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images.","authors":"Jinfeng Wang, Yuhang Chu, Guoqing Chen, Minyi Zhao, Jizhuang Wu, Ritao Qu, Zhentao Wang","doi":"10.34133/plantphenomics.0197","DOIUrl":"https://doi.org/10.34133/plantphenomics.0197","url":null,"abstract":"Due to nutrient stress, which is an important constraint to the development of the global agricultural sector, it is now vital to timely evaluate plant health. Remote sensing technology, especially hyperspectral imaging technology, has evolved from spectral response modes to pattern recognition and vegetation monitoring. This study established a hyperspectral library of 14 NPK (nitrogen, phosphorus, potassium) nutrient stress conditions in rice. The terrestrial hyperspectral camera (SPECIM-IQ) collected 420 rice stress images and extracted as well as analyzed representative spectral reflectance curves under 14 stress modes. The canopy spectral profile characteristics, vegetation index, and principal component analysis demonstrated the differences in rice under different nutrient stresses. A transformer-based deep learning network SHCFTT (SuperPCA-HybridSN-CBAM-Feature tokenization transformer) was established for identifying nutrient stress patterns from hyperspectral images while being compared with classic support vector machines, 1D-CNN (1D-Convolutional Neural Network), and 3D-CNN. The total accuracy of the SHCFTT model under different modeling strategies and different years ranged from 93.92% to 100%, indicating the positive effect of the proposed method on improving the accuracy of identifying nutrient stress in rice.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11266478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141760503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3D morphological features quantification and analysis of corn leaves 玉米叶片的三维形态特征量化与分析

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-07-22 DOI: 10.34133/plantphenomics.0225

Weiliang Wen, Jinglu Wang, Yanxin Zhao, Chuanyu Wang, Kai Liu, Bo Chen, Yuanqiao Wang, Minxiao Duan, Xinyu Guo

引用次数: 0

Visualization and Quantitative Evaluation of Functional Structures of Soybean Root Nodules via Synchrotron X-ray Imaging. 通过同步辐射 X 射线成像对大豆根瘤的功能结构进行可视化和定量评估

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-07-17 eCollection Date: 2024-01-01 DOI: 10.34133/plantphenomics.0203

Alireza Nakhforoosh, Emil Hallin, Chithra Karunakaran, Malgorzata Korbas, Jarvis Stobbs, Leon Kochian

{"title":"Visualization and Quantitative Evaluation of Functional Structures of Soybean Root Nodules via Synchrotron X-ray Imaging.","authors":"Alireza Nakhforoosh, Emil Hallin, Chithra Karunakaran, Malgorzata Korbas, Jarvis Stobbs, Leon Kochian","doi":"10.34133/plantphenomics.0203","DOIUrl":"10.34133/plantphenomics.0203","url":null,"abstract":"The efficiency of N2-fixation in legume-rhizobia symbiosis is a function of root nodule activity. Nodules consist of 2 functionally important tissues: (a) a central infected zone (CIZ), colonized by rhizobia bacteria, which serves as the site of N2-fixation, and (b) vascular bundles (VBs), serving as conduits for the transport of water, nutrients, and fixed nitrogen compounds between the nodules and plant. A quantitative evaluation of these tissues is essential to unravel their functional importance in N2-fixation. Employing synchrotron-based x-ray microcomputed tomography (SR-μCT) at submicron resolutions, we obtained high-quality tomograms of fresh soybean root nodules in a non-invasive manner. A semi-automated segmentation algorithm was employed to generate 3-dimensional (3D) models of the internal root nodule structure of the CIZ and VBs, and their volumes were quantified based on the reconstructed 3D structures. Furthermore, synchrotron x-ray fluorescence imaging revealed a distinctive localization of Fe within CIZ tissue and Zn within VBs, allowing for their visualization in 2 dimensions. This study represents a pioneer application of the SR-μCT technique for volumetric quantification of CIZ and VB tissues in fresh, intact soybean root nodules. The proposed methods enable the exploitation of root nodule's anatomical features as novel traits in breeding, aiming to enhance N2-fixation through improved root nodule activity.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11254386/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141634323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Grain Protein Content Phenotyping in Rice via Hyperspectral Imaging Technology and a Genome-Wide Association Study. 通过高光谱成像技术和全基因组关联研究对水稻谷物蛋白质含量进行表型。

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-07-08 eCollection Date: 2024-01-01 DOI: 10.34133/plantphenomics.0200

Hengbiao Zheng, Weijie Tang, Tao Yang, Meng Zhou, Caili Guo, Tao Cheng, Weixing Cao, Yan Zhu, Yunhui Zhang, Xia Yao

{"title":"Grain Protein Content Phenotyping in Rice via Hyperspectral Imaging Technology and a Genome-Wide Association Study.","authors":"Hengbiao Zheng, Weijie Tang, Tao Yang, Meng Zhou, Caili Guo, Tao Cheng, Weixing Cao, Yan Zhu, Yunhui Zhang, Xia Yao","doi":"10.34133/plantphenomics.0200","DOIUrl":"10.34133/plantphenomics.0200","url":null,"abstract":"Efficient and accurate acquisition of the rice grain protein content (GPC) is important for selecting high-quality rice varieties, and remote sensing technology is an attractive potential method for this task. However, the majority of multispectral sensors are poor predictors of GPC due to their broad spectral bands. Hyperspectral technology provides a new analytical technology for bridging the gap between phenomics and genomics. However, the small size of typical datasets is a constraint for model construction for estimating GPC, limiting their accuracy and reducing their ability to generalize to a wide range of varieties. In this study, we used hyperspectral data of rice grains from 515 japonica varieties and deep convolution generative adversarial networks (DCGANs) to generate simulated data to improve the model accuracy. Features sensitive to GPC were extracted after applying a continuous wavelet transform (CWT), and the estimated GPC model was constructed by partial least squares regression (PLSR). Finally, a genome-wide association study (GWAS) was applied to the measured and generated datasets to detect GPC loci. The results demonstrated that the simulated GPC values generated after 8,000 epochs were closest to the measured values. The wavelet feature (WF1743, 2), obtained from the data with the addition of 200 simulated samples, exhibited the highest GPC estimation accuracy (R 2 = 0.58 and RRMSE = 6.70%). The GWAS analysis showed that the estimated values based on the simulated data detected the same loci as the measured values, including the OsmtSSB1L gene related to grain storage protein. This study provides a new technique for the efficient genetic study of phenotypic traits in rice based on hyperspectral technology.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11227985/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141559569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DEKR-SPrior: An Efficient Bottom-Up Keypoint Detection Model for Accurate Pod Phenotyping in Soybean. DEKR-SPrior：用于精确大豆花苞表型的高效自下而上关键点检测模型

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-06-27 eCollection Date: 2024-01-01 DOI: 10.34133/plantphenomics.0198

Jingjing He, Lin Weng, Xiaogang Xu, Ruochen Chen, Bo Peng, Nannan Li, Zhengchao Xie, Lijian Sun, Qiang Han, Pengfei He, Fangfang Wang, Hui Yu, Javaid Akhter Bhat, Xianzhong Feng

{"title":"DEKR-SPrior: An Efficient Bottom-Up Keypoint Detection Model for Accurate Pod Phenotyping in Soybean.","authors":"Jingjing He, Lin Weng, Xiaogang Xu, Ruochen Chen, Bo Peng, Nannan Li, Zhengchao Xie, Lijian Sun, Qiang Han, Pengfei He, Fangfang Wang, Hui Yu, Javaid Akhter Bhat, Xianzhong Feng","doi":"10.34133/plantphenomics.0198","DOIUrl":"https://doi.org/10.34133/plantphenomics.0198","url":null,"abstract":"The pod and seed counts are important yield-related traits in soybean. High-precision soybean breeders face the major challenge of accurately phenotyping the number of pods and seeds in a high-throughput manner. Recent advances in artificial intelligence, especially deep learning (DL) models, have provided new avenues for high-throughput phenotyping of crop traits with increased precision. However, the available DL models are less effective for phenotyping pods that are densely packed and overlap in in situ soybean plants; thus, accurate phenotyping of the number of pods and seeds in soybean plant is an important challenge. To address this challenge, the present study proposed a bottom-up model, DEKR-SPrior (disentangled keypoint regression with structural prior), for in situ soybean pod phenotyping, which considers soybean pods and seeds analogous to human people and joints, respectively. In particular, we designed a novel structural prior (SPrior) module that utilizes cosine similarity to improve feature discrimination, which is important for differentiating closely located seeds from highly similar seeds. To further enhance the accuracy of pod location, we cropped full-sized images into smaller and high-resolution subimages for analysis. The results on our image datasets revealed that DEKR-SPrior outperformed multiple bottom-up models, viz., Lightweight-OpenPose, OpenPose, HigherHRNet, and DEKR, reducing the mean absolute error from 25.81 (in the original DEKR) to 21.11 (in the DEKR-SPrior) in pod phenotyping. This paper demonstrated the great potential of DEKR-SPrior for plant phenotyping, and we hope that DEKR-SPrior will help future plant phenotyping.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11209727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141470375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Segment Anything for Comprehensive Analysis of Grapevine Cluster Architecture and Berry Properties. 用于全面分析葡萄藤簇结构和浆果特性的分段数据。

IF 7.6 1区农林科学

Plant Phenomics Pub Date : 2024-06-27 eCollection Date: 2024-01-01 DOI: 10.34133/plantphenomics.0202

Efrain Torres-Lomas, Jimena Lado-Bega, Guillermo Garcia-Zamora, Luis Diaz-Garcia

{"title":"Segment Anything for Comprehensive Analysis of Grapevine Cluster Architecture and Berry Properties.","authors":"Efrain Torres-Lomas, Jimena Lado-Bega, Guillermo Garcia-Zamora, Luis Diaz-Garcia","doi":"10.34133/plantphenomics.0202","DOIUrl":"https://doi.org/10.34133/plantphenomics.0202","url":null,"abstract":"Grape cluster architecture and compactness are complex traits influencing disease susceptibility, fruit quality, and yield. Evaluation methods for these traits include visual scoring, manual methodologies, and computer vision, with the latter being the most scalable approach. Most of the existing computer vision approaches for processing cluster images often rely on conventional segmentation or machine learning with extensive training and limited generalization. The Segment Anything Model (SAM), a novel foundation model trained on a massive image dataset, enables automated object segmentation without additional training. This study demonstrates out-of-the-box SAM's high accuracy in identifying individual berries in 2-dimensional (2D) cluster images. Using this model, we managed to segment approximately 3,500 cluster images, generating over 150,000 berry masks, each linked with spatial coordinates within their clusters. The correlation between human-identified berries and SAM predictions was very strong (Pearson's r2 = 0.96). Although the visible berry count in images typically underestimates the actual cluster berry count due to visibility issues, we demonstrated that this discrepancy could be adjusted using a linear regression model (adjusted R 2 = 0.87). We emphasized the critical importance of the angle at which the cluster is imaged, noting its substantial effect on berry counts and architecture. We proposed different approaches in which berry location information facilitated the calculation of complex features related to cluster architecture and compactness. Finally, we discussed SAM's potential integration into currently available pipelines for image generation and processing in vineyard conditions.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11208874/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141470376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulation of Automatically Annotated Visible and Multi-/Hyperspectral Images Using the Helios 3D Plant and Radiative Transfer Modeling Framework. 利用 Helios 3D 植物和辐射传输建模框架模拟自动注释的可见光和多光谱/高光谱图像。

IF 6.5 1区农林科学

Plant Phenomics Pub Date : 2024-05-30 eCollection Date: 2024-01-01 DOI: 10.34133/plantphenomics.0189

Tong Lei, Jan Graefe, Ismael K Mayanja, Mason Earles, Brian N Bailey

{"title":"Simulation of Automatically Annotated Visible and Multi-/Hyperspectral Images Using the Helios 3D Plant and Radiative Transfer Modeling Framework.","authors":"Tong Lei, Jan Graefe, Ismael K Mayanja, Mason Earles, Brian N Bailey","doi":"10.34133/plantphenomics.0189","DOIUrl":"10.34133/plantphenomics.0189","url":null,"abstract":"Deep learning and multimodal remote and proximal sensing are widely used for analyzing plant and crop traits, but many of these deep learning models are supervised and necessitate reference datasets with image annotations. Acquiring these datasets often demands experiments that are both labor-intensive and time-consuming. Furthermore, extracting traits from remote sensing data beyond simple geometric features remains a challenge. To address these challenges, we proposed a radiative transfer modeling framework based on the Helios 3-dimensional (3D) plant modeling software designed for plant remote and proximal sensing image simulation. The framework has the capability to simulate RGB, multi-/hyperspectral, thermal, and depth cameras, and produce associated plant images with fully resolved reference labels such as plant physical traits, leaf chemical concentrations, and leaf physiological traits. Helios offers a simulated environment that enables generation of 3D geometric models of plants and soil with random variation, and specification or simulation of their properties and function. This approach differs from traditional computer graphics rendering by explicitly modeling radiation transfer physics, which provides a critical link to underlying plant biophysical processes. Results indicate that the framework is capable of generating high-quality, labeled synthetic plant images under given lighting scenarios, which can lessen or remove the need for manually collected and annotated data. Two example applications are presented that demonstrate the feasibility of using the model to enable unsupervised learning by training deep learning models exclusively with simulated images and performing prediction tasks using real images.","PeriodicalId":20318,"journal":{"name":"Plant Phenomics","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11136674/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141180561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CucumberAI: Cucumber fruit morphology identification system based on artificial intelligence CucumberAI：基于人工智能的黄瓜果实形态识别系统

IF 6.5 1区农林科学

Plant Phenomics Pub Date : 2024-05-19 DOI: 10.34133/plantphenomics.0193

Wei Xue, Haifeng Ding, Tao Jin, Jialing Meng, Shiyou Wang, Zuo Liu, Xiupeng Ma, Ji Li

引用次数: 0