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Crop phenotyping in a context of global change:What to measure and how to do it

Journal of Integrative Plant Biology 2022年第2期64卷 592-618页

作者： Jose Luis Araus shawn carlisle kefauver Omar Vergara-Díaz Adrian Gracia-Romero Fatima Zahra Rezzouk Joel Segarra Maria Luisa Buchaillot Melissa Chang-Espino Thomas Vatter Rut Sanchez-Bragado JoséArmando Fernandez-Gallego Maria Dolores Serret Jordi Bort Integrative Crop Ecophysiology Group Plant Physiology SectionFaculty of BiologyUniversity of BarcelonaBarcelonaSpain AGROTECNIO Center LleidaSpain Plant Ecophysiology and Metabolism Lab ITQB NovaPlant Sciences DivisionOeirasPortugal Programa de Ingeniería Electrónica Facultad de IngenieríaUniversidad de IbaguéIbaguéColombia

High-throughput crop phenotyping,particularly under field conditions,is nowadays perceived as a key factor limiting crop genetic advance.Phenotyping not only facilitates conventional breeding,but it is necessary to fully exploit the capabilities of molecular breeding,and it can be exploited to predict breeding targets for the years ahead at the regional level through more advanced simulation models and decision support systems.In terms of phenotyping,it is necessary to determined which selection traits are relevant in each situation,and which phenotyping tools/methods are available to assess such traits.Remote sensing methodologies are currently the most popular approaches,even when lab-based analyses are still relevant in many circumstances.On top of that,data processing and automation,together with machine learning/deep learning are contributing to the wide range of applications for phenotyping.This review addresses spectral and red-green-blue sensing as the most popular remote sensing approaches,alongside stable isotope composition as an example of a lab-based tool,and root phenotyping,which represents one of the frontiers for field phenotyping.Further,we consider the two most promising forms of aerial platforms(unmanned aerial vehicle and satellites)and some of the emerging data-processing techniques.The review includes three Boxes that examine specific case studies.

关键词： crop phenotyping deep learning models photosynthesis platforms remote sensing roots satellites sensors stable isotopes

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Multiscale assessment of ground,aerial and satellite spectral data for monitoring wheat grain nitrogen content

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Information Processing in Agriculture 2023年第4期10卷 504-522页

作者： Joel Segarra Fatima Zahra Rezzouk Nieves Aparicio Jon González-Torralba Iker Aranjuelo Adrian Gracia-Romero Jose Luis Araus shawn C.kefauver Integrative Crop Ecophysiology Group Plant Physiology SectionFaculty of BiologyUniversity of BarcelonaBarcelona 08028Spain AGROTECNIO(Center for Research in Agrotechnology) Lleida 251981Spain Agro-technological Institute of Castilla y León(ITACyL) Valladolid 47071Spain Grupo AN Tajonar 31192NavarreSpain Instituto de Agrobiotecnología(IdAB) CSIC-Gobierno de NavarraMutilva Baja 31192PamplonaNavarreSpain

Wheat grain quality characteristics have experienced increasing attention as a central factor affecting wheat end-use products quality and human health.Nonetheless,in the last decades a reduction in grain quality has been observed.Therefore,it is central to develop efficient quality-related phenotyping tools.In this sense,one of the most relevant wheat features related to grain quality traits is grain nitrogen content,which is directly linked to grain protein content and monitorable with remote sensing approaches.Moreover,the relation between nitrogen fertilization and grain nitrogen content(protein)plays a central role in the sustainability of agriculture.Both aiming to develop efficient phenotyping tools using remote sensing instruments and to advance towards a field-level efficient and sustainable monitoring of grain nitrogen status,this paper studies the efficacy of various sensors,multispectral and visible red-greenblue(RGB),at different scales,ground and unmanned aerial vehicle(UAV),and phenological stages(anthesis and grain filling)to estimate grain nitrogen content.Linear models were calculated using vegetation indices at each sensing level,sensor type and phenological stage.Furthermore,this study explores the up-scalability of the best performing model to satellite level Sentinel-2 equivalent data.We found that models built at the phenological stage of anthesis with UAV-level multispectral cameras using red-edge bands outperformed grain nitrogen content estimation(R2=0.42,RMSE=0.18%)in comparison with those models built with RGB imagery at ground and aerial level,as well as with those built with widely used ground-level multispectral sensors.We also demonstrated the possibility to use UAV-built multispectral linear models at the satellite scale to determine grain nitrogen content effectively(R2=0.40,RMSE=0.29%)at actual wheat fields.

关键词： Wheat Remote sensing Sentinel-2 Grain nitrogen content Phenotyping

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Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions

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The Crop Journal 2015年第3期3卷 200-210页

作者： Omar Vergara-Diaz shawn C.kefauver Abdelhalim Elazab Maria Teresa Nieto-Taladriz Jose Luis Araus Unit of Plant Physiology Department of Plant BiologyFaculty of BiologyUniversity of BarcelonaDiagonal 64508028 BarcelonaSpain National Institute for Agricultural and Food Research and Technology(INIA) Ctra de la Coruna 7.528040Madrid Spain

The biotrophic fungus Puccinia striiformis f. sp. tritici is the causal agent of the yellow rust in wheat. Between the years 2010–2013 a new strain of this pathogen(Warrior/Ambition),against which the present cultivated wheat varieties have no resistance, appeared and spread rapidly. It threatens cereal production in most of Europe. The search for sources of resistance to this strain is proposed as the most efficient and safe solution to ensure high grain production. This will be helped by the development of high performance and low cost techniques for field phenotyping. In this study we analyzed vegetation indices in the Red,Green, Blue(RGB) images of crop canopies under field conditions. We evaluated their accuracy in predicting grain yield and assessing disease severity in comparison to other field measurements including the Normalized Difference Vegetation Index(NDVI), leaf chlorophyll content, stomatal conductance, and canopy temperature. We also discuss yield components and agronomic parameters in relation to grain yield and disease severity.RGB-based indices proved to be accurate predictors of grain yield and grain yield losses associated with yellow rust(R2= 0.581 and R2= 0.536, respectively), far surpassing the predictive ability of NDVI(R2= 0.118 and R2= 0.128, respectively). In comparison to potential yield, we found the presence of disease to be correlated with reductions in the number of grains per spike, grains per square meter, kernel weight and harvest index. Grain yield losses in the presence of yellow rust were also greater in later heading varieties. The combination of RGB-based indices and days to heading together explained 70.9% of the variability in grain yield and 62.7% of the yield losses.

关键词： Wheat yellow rust Field phenotyping NDVI Phenology,Puccinia striiformis RGB-based indices Triticum durum

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