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Frost Damage Index:The Antipode of Growing Degree Days

Plant Phenomics 2023年第4期5卷 757-766页

作者： Flavian Tschurr norbert kirchgessner Andreas Hund Lukas Kronenberg Jonas Anderegg Achim Walter Lukas Roth Department of Environmental System Sciences Institute of Agricultural SciencesETH ZürichZürichSwitzerland Crop Genetics John Innes CentreNorwichUK Department of Environmental System Sciences Institute of Integrative BiologyETH ZürichZürichSwitzerland

Abiotic stresses such as heat and frost limit plant growth and ***-based field phenotyping methods allow quantifying not only plant growth but also plant *** crops show senescence caused by cold spells,visible as declines in leaf *** accurately quantified such declines by monitoring changes in canopy cover based on time-resolved high-resolution imagery in the ***-six winter wheat genotypes were measured in multiple years.A concept termed"frost damage index"(FDI)was developed that,in analogy to growing degree days,summarizes frost events in a cumulative *** measured sensitivity of genotypes to the FDI correlated with visual scorings commonly used in breeding to assess winter *** FDI concept could be adapted to other factors such as drought or heat *** commonly not considered in plant growth modeling,integrating such degradation processes may be key to improving the prediction of plant performance for future climate scenarios.

关键词： winter analogy integrating

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Repeated Multiview Imaging for Estimating Seedling Tiller Counts of Wheat Genotypes Using Drones

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Plant Phenomics 2020年第1期2卷 279-298页

作者： Lukas Roth Moritz Camenzind Helge Aasen Lukas Kronenberg Christoph Barendregt Karl-Heinz Camp Achim Walter norbert kirchgessner Andreas Hund ETH Zurich Institute of Agricultural SciencesUniversitätstrasse 28092 ZurichSwitzerland Delley Samen und Pflanzen AG Route de Portalban 401567 DelleySwitzerland

Early generation breeding nurseries with thousands of genotypes in single-row plots are well suited to capitalize on high throughput ***,methods to monitor the intrinsically hard-to-phenotype early development of wheat are yet *** aimed to develop proxy measures for the rate of plant emergence,the number of tillers,and the beginning of stem elongation using drone-based *** used RGB images(ground sampling distance of 3mm pixel-1)acquired by repeated flights(≥2 flights per week)to quantify temporal changes of visible leaf *** exploit the information contained in the multitude of viewing angles within the RGB images,we processed them to multiview ground cover images showing plant pixel *** on these images,we trained a support vector machine for the beginning of stem elongation(GS30).Using the GS30 as key point,we subsequently extracted plant and tiller counts using a watershed algorithm and growth modeling,*** results show that determination coefficients of predictions are moderate for plant count(R^(2)=0:52),but strong for tiller count(R^(2)=0:86)and GS30(R^(2)=0:77).Heritabilities are superior to manual measurements for plant count and tiller count,but inferior for GS30 *** the selection intensity due to throughput may overcome this *** image traits can replace hand measurements with high efficiency(85-223%).We therefore conclude that multiview images have a high potential to become a standard tool in plant phenomics.

关键词： Wheat watershed breeding

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Global Wheat Head Detection(GWHD)Dataset:A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods

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Plant Phenomics 2020年第1期2卷 243-254页

作者： Etienne David Simon Madec Pouria Sadeghi-Tehran Helge Aasen Bangyou Zheng Shouyang Liu norbert kirchgessner Goro Ishikawa Koichi Nagasawa Minhajul A.Badhon Curtis Pozniak Benoit de Solan Andreas Hund Scott C.Chapman Frédéric Baret Ian Stavness Wei Guo Arvalis Institut du végétal3 Rue Joseph et Marie Hackin75116 ParisFrance UMR1114 EMMAH INRAECentre PACABâtiment ClimatDomaine Saint-Paul228 Route de l’AérodromeCS 4050984914 Avignon CedexFrance Plant Sciences Department Rothamsted ResearchHarpendenUK Institute of Agricultural Sciences ETH ZurichUniversitätstrasse 28092 ZurichSwitzerland CSIRO Agriculture and Food Queensland Biosciences Precinct306 Carmody RoadSt Lucia4067 QLDAustralia Plant Phenomics Research Center Nanjing Agricultural UniversityNanjingChina Institute of Crop Science National Agriculture and Food Research OrganizationJapan Hokkaido Agricultural Research Center National Agriculture and Food Research OrganizationJapan Department of Computer Science University of SaskatchewanCanada Department of Plant Sciences University of SaskatchewanCanada School of Food and Agricultural Sciences The University of QueenslandGatton4343 QLDAustralia Graduate School of Agricultural and Life Sciences The University of Tokyo1-1-1 Midori-choNishitokyo CityTokyoJapan

The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health,size,maturity stage,and the presence of *** studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning ***,these methods have generally been calibrated and validated on limited *** variability in observational conditions,genotypic differences,development stages,and head orientation makes wheat head detection a challenge for computer ***,possible blurring due to motion or wind and overlap between heads for dense populations make this task even more *** a joint international collaborative effort,we have built a large,diverse,and well-labelled dataset of wheat images,called the Global Wheat Head Detection(GWHD)*** contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of *** for image acquisition,associating minimum metadata to respect FAIR principles,and consistent head labelling methods are proposed when developing new head detection *** GWHD dataset is publicly available at http://***/and aimed at developing and benchmarking methods for wheat head detection.

关键词： Wheat wheat maturity

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Global Wheat Head Detection 2021:An Improved Dataset for Benchmarking Wheat Head Detection Methods

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Plant Phenomics 2021年第1期3卷 277-285页

作者： Etienne David Mario Serouart Daniel Smith Simon Madec Kaaviya Velumani Shouyang Liu Xu Wang Francisco Pinto Shahameh Shafiee Izzat SATahir Hisashi Tsujimoto Shuhei Nasuda Bangyou Zheng norbert kirchgessner Helge Aasen Andreas Hund Pouria Sadhegi-Tehran Koichi Nagasawa Goro Ishikawa Sébastien Dandrifosse Alexis Carlier Benjamin Dumont Benoit Mercatoris Byron Evers Ken Kuroki Haozhou Wang Masanori Ishii Minhajul ABadhon Curtis Pozniak David Shaner LeBauer Morten Lillemo Jesse Poland Scott Chapman Benoit de Solan Frédéric Baret Ian Stavness Wei Guo Arvalis Institut du Végétal3 Rue Joseph et Marie Hackin75116 ParisFrance UMR1114 EMMAH INRAECentre PACABâtiment ClimatDomaine Saint-Paul228 Route de l’AérodromeCS 4050984914 Avignon CedexFrance School of Food and Agricultural Sciences The University of QueenslandGatton4343 QLDAustralia Hiphen SAS 120 Rue Jean DaussetAgroparcBâtiment Technicité84140 AvignonFrance Plant Phenomics Research Center Nanjing Agricultural UniversityNanjingChina Wheat Genetics Resource Center Dep.of Plant PathologyKansas State Univ.4024 Throckmorton Plant Sciences CenterManhattanKansasUSA Global Wheat Program International Maize and Wheat Improvement Centre(CIMMYT)MexicoD.F.Mexico Faculty of Biosciences Norwegian University of Life SciencesP.O.Box 5003NO-1432ÅsNorway Agricultural Research Corporation Wheat Research ProgramP.O.Box 126Wad MedaniSudan Arid Land Research Center Tottori UniversityTottori 680-0001Japan Laboratories of Plant Genetics and Plant Breeding Graduate School of AgricultureKyoto UniversityJapan CSIRO Agriculture and Food Queensland Biosciences Precinct306 Carmody RoadSt Lucia4067 QLDAustralia Institute of Agricultural Sciences ETH ZurichUniversitätstrasse 28092 ZurichSwitzerland Plant Sciences Department Rothamsted ResearchHarpendenUK Institute of Crop Science National Agriculture and Food Research OrganizationJapan Hokkaido Agricultural Research Center National Agriculture and Food Research OrganizationJapan Biosystems Dynamics and Exchanges TERRA Teaching and Research CenterGembloux Agro-Bio TechUniversity of Liège5030 GemblouxBelgium Plant Sciences TERRA Teaching and Research CenterGembloux Agro-Bio TechUniversity of Liège5030 GemblouxBelgium Graduate School of Agricultural and Life Sciences The University of Tokyo1-1-1 Midori-choNishitokyo CityTokyoJapan Department of Computer Science University of SaskatchewanCanada Department of Plant Sciences University of SaskatchewanCanada College of Agriculture and Life Sciences University of ArizonaTucsonArizona

The Global Wheat Head Detection(GWHD)dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/*** an associated competition hosted in Kaggle,GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science *** this first experience,a few avenues for improvements have been identified regarding data size,head diversity,and label *** address these issues,the 2020 dataset has been reexamined,relabeled,and complemented by adding 1722 images from 5 additional countries,allowing for 81,553 additional wheat *** now release in 2021 a new version of the Global Wheat Head Detection dataset,which is bigger,more diverse,and less noisy than the GWHD_2020 version.

关键词： Wheat adding release

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