http://oar.icrisat.org/id/eprint/11602 2020-09-06T11:48:46Z info:eu-repo/semantics/descriptiveMetadata http://oar.icrisat.org/11602/ High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms Jin, X Zarco-Tejada, P Schmidhalter, U Reynolds, M P Hawkesford, M J Varshney, R K Yang, T Nie, C Li, Z Ming, B Xiao, Y Xie, Y Li, S Plant Breeding Genetics and Genomics Climate Change Food Security Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields. 2020-07 Article PeerReviewed application/pdf en http://oar.icrisat.org/11602/1/paper115.pdf Jin, X and Zarco-Tejada, P and Schmidhalter, U and Reynolds, M P and Hawkesford, M J and Varshney, R K and Yang, T and Nie, C and Li, Z and Ming, B and Xiao, Y and Xie, Y and Li, S (2020) High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms. IEEE Geoscience and Remote Sensing Magazine. pp. 1-33. ISSN 2473-2397 https://doi.org/10.1109/MGRS.2020.2998816 doi:10.1109/MGRS.2020.2998816 info:eu-repo/semantics/objectFile info:eu-repo/semantics/humanStartPage