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| 1 | 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显示文摘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 awns.Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms.However,these methods have generally been calibrated and validated on limited datasets.High variability in observational conditions,genotypic differences,development stages,and head orientation makes wheat head detection a challenge for computer vision.Further,possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex.Through 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)dataset.It 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 genotypes.Guidelines for image acquisition,associating minimum metadata to respect FAIR principles,and consistent head labelling methods are proposed when developing new head detection datasets.The GWHD dataset is publicly available at http://gffzzef49fa24bca249bbhfo5ocfnn6qcw66uq.ffgz.tsg.suse.edu.cn/and aimed at developing and benchmarking methods for wheat head detection. | 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 | 2020 | Plant Phenomics2020，2，1： | 13 |
| 2 | Global Wheat Head Detection 2021:An Improved Dataset for Benchmarking Wheat Head Detection Methods显示文摘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/institutions.With an associated competition hosted in Kaggle,GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities.From this first experience,a few avenues for improvements have been identified regarding data size,head diversity,and label reliability.To 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 heads.We 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. | 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 | 2021 | Plant Phenomics2021，3，1： | 2 |
| 3 | Repeated Multiview Imaging for Estimating Seedling Tiller Counts of Wheat Genotypes Using Drones显示文摘Early generation breeding nurseries with thousands of genotypes in single-row plots are well suited to capitalize on high throughput phenotyping.Nevertheless,methods to monitor the intrinsically hard-to-phenotype early development of wheat are yet rare.We aimed to develop proxy measures for the rate of plant emergence,the number of tillers,and the beginning of stem elongation using drone-based imagery.We 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 area.To 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 fractions.Based 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,respectively.Our 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 measurements.Increasing the selection intensity due to throughput may overcome this limitation.Multiview 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. | Lukas Roth Moritz Camenzind Helge Aasen Lukas Kronenberg Christoph Barendregt Karl-Heinz Camp Achim Walter Norbert Kirchgessner Andreas Hund | 2020 | Plant Phenomics2020，2，1： | 2 |
| 4 | Precision Phenotyping Reveals Novel Loci for Quantitative Resistance to Septoria Tritici Blotch显示文摘Accurate,high-throughput phenotyping for quantitative traits is a limiting factor for progress in plant breeding.We developed an automated image analysis to measure quantitative resistance to septoria tritici blotch(STB),a globally important wheat disease,enabling identification of small chromosome intervals containing plausible candidate genes for STB resistance.335 winter wheat cultivars were included in a replicated field experiment that experienced natural epidemic development by a highly diverse but fungicide-resistant pathogen population.More than 5.4 million automatically generated phenotypes were associated with 13,648 SNP markers to perform the GWAS.We identified 26 chromosome intervals explaining 1.9-10.6%of the variance associated with four independent resistance traits.Sixteen of the intervals overlapped with known STB resistance intervals,suggesting that our phenotyping approach can identify simultaneously(i.e.,in a single experiment)many previously defined STB resistance intervals.Seventeen of the intervals were less than 5 Mbp in size and encoded only 173 genes,including many genes associated with disease resistance.Five intervals contained four or fewer genes,providing high priority targets for functional validation.Ten chromosome intervals were not previously associated with STB resistance,perhaps representing resistance to pathogen strains that had not been tested in earlier experiments.The SNP markers associated with these chromosome intervals can be used to recombine different forms of quantitative STB resistance that are likely to be more durable than pyramids of major resistance genes.Our experiment illustrates how high-throughput automated phenotyping can accelerate breeding for quantitative disease resistance. | Steven Yates Alexey Mikaberidze Simon GKrattinger Michael Abrouk Andreas Hund Kang Yu Bruno Studer Simone Fouche Lukas Meile Danilo Pereira Petteri Karisto Bruce A.McDonald | 2019 | Plant Phenomics2019，1，1： | 2 |
| 5 | Rooting depth and water use efficiency of tropical maize inbred lines, differing in drought tolerance显示文摘 | Andreas Hund Nathinee Ruta Markus Liedgens | 2009 | Plant and Soil (-)2009，，1： | 2 |
| 6 | Characterization of cerebral microang/opathy using 3 Tesla MRI: correlation with neurological impairment and vascular risk factors 显示文摘 | Hund - Georgiadis M Ballaschke O Scheid R | 2002 | J Magn Reson Imaging2002，15，： | 1 |
| 7 | Default probability dynamics in Structural Models显示文摘 | Hund J | 2003 | The Journal of Fixed-Income2003，9，： | 1 |
| 8 | Uncertainty about average profitability and the diversification discount显示文摘 | Hund J Monk D Tice S | 2010 | Journal of Financial Economics2010，96，3： | 1 |
| 9 | Cold tolerance ofmaize seedlings as determined by root morphology and photosyn-thetic traits显示文摘 | Hund A Fracheboud Y Soldati A | 2008 | European Journal of Agronomy2008，28，： | 1 |
| 10 | Neurological complications of sepsis: critical illness polyneuropathy and myopathy显示文摘 | Hund E | 2001 | J Neurol2001，248，： | 1 |
| 11 | Rooting depth and water use efficiency of tropical maize inbred lines, differing in drought tolerance显示文摘 | Andreas Hund Nathinee Ruta Markus Liedgens | 2009 | Plant and Soil . 2009 (1-2)2009，，1： | 1 |
| 12 | Estimating systemic risk in the international financial system显示文摘 | BARTRAM S M BROWN G W HUND J E | 2007 | Journal of Financial Economics2007，86，： | 1 |
| 13 | Role of CaMK Ⅱ in cardiovascular health,disease and arrhythmia显示文摘 | Mohler PJ Hund J | | Heart Rhythm0，281，： | 1 |
| 14 | Cortical reafferentation following left subcortical hemorrhage: a serial functional MR study 显示文摘 | Hund Georgiadis M Norris DG von Cramon DY | 2000 | Neurology2000，55，： | 1 |
| 15 | Prediction of residues in discontinuous B cell epitopes using protein 3D structures显示文摘 | ANDERSEN H NIEI SEN M HUND O | 2006 | Protein Sci2006，15，11： | 1 |
| 16 | Atonic pupil after cataract surgery 显示文摘 | Golnik KC Hund PW 3rd Apple DJ | 1995 | J Cataract Refract Surg1995，21，2： | 1 |
| 17 | Understanding critical illness myopathy:approaching the pathomechanism显示文摘 | Friedrich O Fink RH Hund E | 2005 | J Nutr2005，135，7： | 1 |
| 18 | QTL controlling root and shoot traits of maize seedlings under cold stress 显示文摘 | Hund A Fracheboud Y Soldati A | 2004 | Theoretical and Applied Genetics2004，109，3： | 1 |
| 19 | Intensive management and treatment of severe Guillain-Barre syndrome显示文摘 | Hund EF Borel CO Cornblath DR | 1999 | Crit Care Med1999，21，： | 1 |
| 20 | Role of CaMKII in cardiovascular health,discasc,and arrhythmia显示文摘 | Mohler P J Hund T J | | 0，，01： | 1 |
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