Araus J L, Kefauver S C, Vergara-Díaz O, Gracia-Romero A, Rezzouk F Z, Segarra J, Buchaillot M L, Chang-Espino M, Vatter T, Sanchez-Bragado R. 2022. Crop phenotyping in a context of global change: What to measure and how to do it. Journal of Integrative Plant Biology, 64, 592–618.

Bai Y, Nie C, Wang H, Cheng M, Liu S, Yu X, Shao M, Wang Z, Wang S, Tuohuti N. 2022. A fast and robust method for plant count in sunflower and maize at different seedling stages using high-resolution UAV RGB imagery. Precision Agriculture, 23, 1720–1742.

Chardon F, Virlon B M, Moreau L, Falque M, Joets J, Decousset L, Murigneux A, Charcosset A. 2004. Genetic architecture of flowering time in maize as inferred from quantitative trait loci meta-analysis and synteny conservation with the rice genome. Genetics, 168, 2169–2185.

Che Y, Wang Q, Xie Z, Zhou L, Li S, Hui F, Wang X, Li B, Ma Y. 2020. Estimation of maize plant height and leaf area index dynamics using an unmanned aerial vehicle with oblique and nadir photography. Annals of Botany, 126, 765–773.

Chen Z, Li W, Gaines C, Buck A, Galli M, Gallavotti A. 2021. Structural variation at the maize WUSCHEL1 locus alters stem cell organization in inflorescences. Nature Communications, 12, 2378.

Cheng M, Jiao X, Liu Y, Shao M, Yu X, Bai Y, Wang Z, Wang S, Tuohuti N, Liu S. 2022. Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning. Agricultural Water Management, 264, 107530.

Cook B D, Bolstad P V, Næsset E, Anderson R S, Garrigues S, Morisette J T, Nickeson J, Davis K J. 2009. Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations. Remote Sensing of Environment, 113, 2366–2379.

Cui D, Cui G, Yang R, She W, Liu Y, Wang H, Su X, Wang J, Liu W, Wang X. 2021. Phenotypic characteristics of ramie (Boehmeria nivea L.) germplasm resources based on UAV remote sensing. Genetic Resources and Crop Evolution, 68, 551–566.

Diepenbrock C H, Tang T, Jines M, Technow F, Lira S, Podlich D, Cooper M, Messina C. 2022. Can we harness digital technologies and physiology to hasten genetic gain in US maize breeding? Plant Physiology, 188, 1141–1157.

Du L, Zhang H, Xin W, Ma K, Du D, Yu C, Liu Y. 2021. Dissecting the genetic basis of flowering time and height related-traits using two doubled haploid populations in maize. Plants, 10, 1585.

Duan T, Zheng B, Guo W, Ninomiya S, Guo Y, Chapman S C. 2016. Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV. Functional Plant Biology, 44, 169–183.

Duncan W G, Williams W A, Loomis R S. 1967. Tassels and the productivity of maize. Crop Science, 7, 37–39.

Fei S, Hassan M A, Xiao Y, Su X, Chen Z, Cheng Q, Duan F, Chen R, Ma Y. 2023. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Precision Agriculture, 24, 187–212.

Gilliot J, Michelin J, Hadjard D, Houot S. 2021. An accurate method for predicting spatial variability of maize yield from UAV-based plant height estimation: A tool for monitoring agronomic field experiments. Precision Agriculture, 22, 897–921.

Guo F, Hou L, Ma C, Li G, Lin R, Zhao Y, Wang X. 2021. Comparative transcriptome analysis of the peanut semi-dwarf mutant 1 reveals regulatory mechanism involved in plant height. Gene, 791, 145722.

Guo Y, Fu Y H, Chen S, Bryant C R, Li X, Senthilnath J, Sun H, Wang S, Wu Z, de Beurs K. 2021. Integrating spectral and textural information for identifying the tasseling date of summer maize using UAV based RGB images. International Journal of Applied Earth Observation and Geoinformation, 102, 102435.

Han L, Yang G, Yang H, Xu B, Li Z, Yang X. 2018. Clustering field-based maize phenotyping of plant-height growth and canopy spectral dynamics using a UAV remote-sensing approach. Frontiers in Plant Science, 9, 1638.

van Heerwaarden J , Doebley J, Briggs W H, Glaubitz J C, Goodman M M, de Jesus Sanchez Gonzalez J, Ross-Ibarra J. 2011. Genetic signals of origin, spread, and introgression in a large sample of maize landraces. Proceedings of the National Academy of Sciences of the United States of America, 108, 1088–1092.

Hu P, Chapman S C, Wang X, Potgieter A, Duan T, Jordan D, Guo Y, Zheng B. 2018. Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: Example for sorghum breeding. European Journal of Agronomy, 95, 24–32.

Jiang Z, Tu H, Bai B, Yang C, Zhao B, Guo Z, Liu Q, Zhao H, Yang W, Xiong L. 2021. Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress. New Phytologist, 232, 440–455.

Jin X, Zarco-Tejada P J, Schmidhalter U, Reynolds M P, Hawkesford M J, Varshney R K, Yang T, Nie C, Li Z, Ming B. 2021. High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms. IEEE Geoscience and Remote Sensing Magazine, 9, 200–231.

Karna Y K, Hussin Y A, Gilani H, Bronsveld M, Murthy M, Qamer F M, Karky B S, Bhattarai T, Aigong X, Baniya C B. 2015. Integration of WorldView–2 and airborne LiDAR data for tree species level carbon stock mapping in Kayar Khola watershed, Nepal. International Journal of Applied Earth Observation and Geoinformation, 38, 280–291.

Kumar P, Choudhary M, Halder T, Prakash N R, Singh V, Vineeth T V, Sheoran S, Longmei N, Rakshit S, Siddique K H. 2022. Salinity stress tolerance and omics approaches: Revisiting the progress and achievements in major cereal crops. Heredity, 128, 497–518.

Li X, Zhou Z, Ding J, Wu Y, Zhou B, Wang R, Ma J, Wang S, Zhang X, Xia Z. 2016. Combined linkage and association mapping reveals QTL and candidate genes for plant and ear height in maize. Frontiers in Plant Science, 7, 833.

Li Z, Zhang X, Zhao Y, Li Y, Zhang G, Peng Z, Zhang J. 2018. Enhancing auxin accumulation in maize root tips improves root growth and dwarfs plant height. Plant Biotechnology Journal, 16, 86–99.

Lin Y, Jiang X, Hu H, Zhou K, Wang Q, Yu S, Yang X, Wang Z, Wu F, Liu S. 2021. QTL mapping for grain number per spikelet in wheat using a high-density genetic map. The Crop Journal, 9, 1108–1114.

Liu M, He W, Zhang A, Zhang L, Sun D, Gao Y, Ni P, Ma X, Cui Z, Ruan Y. 2021. Genetic analysis of maize shank length by QTL mapping in three recombinant inbred line populations. Plant Science, 303, 110767.

Liu S, Jin X, Nie C, Wang S, Yu X, Cheng M, Shao M, Wang Z, Tuohuti N, Bai Y. 2021. Estimating leaf area index using unmanned aerial vehicle data: Shallow vs. deep machine learning algorithms. Plant Physiology, 187, 1551–1576.

Liu T, He J, Dong K, Wang X, Wang W, Yang P, Ren R, Zhang L, Zhang Z, Yang T. 2020. QTL mapping of yield component traits on bin map generated from resequencing a RIL population of foxtail millet (Setaria italica). BMC Genomics, 21, 1–13.

Lu J, Cheng D, Geng C, Zhang Z, Xiang Y, Hu T. 2021. Combining plant height, canopy coverage and vegetation index from UAV-based RGB images to estimate leaf nitrogen concentration of summer maize. Biosystems Engineering, 202, 42–54.

Lu L, Luo J, Xin Y, Duan H, Sun Z, Qiu Y, Xiao Q. 2022. How can UAV contribute in satellite-based Phragmites australis aboveground biomass estimating? International Journal of Applied Earth Observation and Geoinformation, 114, 103024.

Luo S, Liu W, Zhang Y, Wang C, Xi X, Nie S, Ma D, Lin Y, Zhou G. 2021. Maize and soybean heights estimation from unmanned aerial vehicle (UAV) LiDAR data. Computers and Electronics in Agriculture, 182, 106005.

Luo S, Wang C, Xi X, Pan F, Qian M, Peng D, Nie S, Qin H, Lin Y. 2017. Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 58, 107–117.

Lv D, Zhang C, Yv R, Yao J, Wu J, Song X, Jian J, Song P, Zhang Z, Han D. 2021. Utilization of a wheat50K SNP microarray-derived high-density genetic map for QTL mapping of plant height and grain traits in wheat. Plants, 10, 1167.

Madec S, Baret F, De Solan B, Thomas S, Dutartre D, Jezequel S, Hemmerlé M, Colombeau G, Comar A. 2017. High-throughput phenotyping of plant height: Comparing unmanned aerial vehicles and ground LiDAR estimates. Frontiers in Plant Science, 8, 2002.

Malambo L, Popescu S C, Murray S C, Putman E, Pugh N A, Horne D W, Richardson G, Sheridan R, Rooney W L, Avant R. 2018. Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery. International Journal of Applied Earth Observation and Geoinformation, 64, 31–42.

Maresma Á, Ariza M, Martínez E, Lloveras J, Martínez-Casasnovas J A. 2016. Analysis of vegetation indices to determine nitrogen application and yield prediction in maize (Zea mays L.) from a standard UAV service. Remote Sensing, 8, 973.

Popescu S C. 2007. Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy, 31, 646–655.

Pugh N A, Horne D W, Murray S C, Carvalho Jr G, Malambo L, Jung J, Chang A, Maeda M, Popescu S, Chu T. 2018. Temporal estimates of crop growth in sorghum and maize breeding enabled by unmanned aerial systems. The Plant Phenome Journal, 1, 1–10.

Qin Y, Li S, Vu T T, Niu Z, Ban Y. 2015. Synergistic application of geometric and radiometric features of LiDAR data for urban land cover mapping. Optics Express, 23, 13761–13775.

Radanović S N, Živanović T B, Sečanski M D, Jovanović S V, Prodanović S A, Šurlan M G G. 2015. The genetic basis of inheritance of plant height in maize. Journal of Agricultural Sciences (Belgrade), 60, 97–107.

Ren J, Zhang X, Li Z, Wu P. 2022. Genetic analysis of maternal haploid inducibility for in vivo haploid induction in maize. Agriculture, 12, 845.

Rodene E, Xu G, Palali Delen S, Zhao X, Smith C, Ge Y, Schnable J, Yang J. 2022. A UAV-based high-throughput phenotyping approach to assess time-series nitrogen responses and identify trait-associated genetic components in maize. The Plant Phenome Journal, 5, e20030.

Shakoor N, Lee S, Mockler T C. 2017. High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Current Opinion in Plant Biology, 38, 184–192.

Shi Y, Thomasson J A, Murray S C, Pugh N A, Rooney W L, Shafian S, Rajan N, Rouze G, Morgan C L, Neely H L. 2016. Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PLoS ONE, 11, e0159781.

Shiferaw B, Smale M, Braun H J, Duveiller E, Reynolds M, Muricho G. 2013. Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Security, 5, 291–317.

Shu M, Shen M, Dong Q, Yang X, Li B, Ma Y. 2022. Estimating the maize above-ground biomass by constructing the tridimensional concept model based on UAV-based digital and multi-spectral images. Field Crops Research, 282, 108491.

Teng F, Zhai L, Liu R, Bai W, Wang L, Huo D, Tao Y, Zheng Y, Zhang Z. 2013. ZmGA3ox2, a candidate gene for a major QTL, qPH3.1, for plant height in maize. The Plant Journal, 73, 405–416.

Veldboom L R, Lee M. 1996. Genetic mapping of quantitative trait loci in maize in stress and nonstress environments: I. Grain yield and yield components. Crop Science, 36, 1310–1319.

Wang W, Guo W, Le L, Yu J, Wu Y, Li D, Wang Y, Wang H, Lu X, Qiao H. 2023. Integration of high-throughput phenotyping, GWAS, and predictive models reveals the genetic architecture of plant height in maize. Molecular Plant, 16, 354–373.

Wang X, Zhang R, Song W, Han L, Liu X, Sun X, Luo M, Chen K, Zhang Y, Yang H. 2019. Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV). Scientific Reports, 9, 1–10.

Wang Z, Wang J, Peng J, Du X, Jiang M, Li Y, Han F, Du G, Yang H, Lian S. 2019a. QTL mapping for 11 agronomic traits based on a genome-wide Bin-map in a large F2 population of foxtail millet (Setaria italica (L.) P. Beauv). Molecular Breeding, 39, 1–13.

Wang Z, Zhang Z, Tang H, Zhang Q, Zhou G, Li X. 2019b. High-density genetic map construction and QTL mapping of leaf and needling traits in Ziziphus jujuba mill. Frontiers in Plant Science, 10, 1424.

Watanabe K, Guo W, Arai K, Takanashi H, Kajiya-Kanegae H, Kobayashi M, Yano K, Tokunaga T, Fujiwara T, Tsutsumi N. 2017. High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Frontiers in Plant Science, 8, 421.

Xie J, Fernandes S B, Mayfield-Jones D, Erice G, Choi M, E Lipka A, Leakey A D. 2021. Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping. Plant Physiology, 187, 1462–1480.

Yan A, Ning S, Geng H, Guo T, Xiao S. 2023. Quantitative trait locus (QTL) mapping for common wheat plant heights based on unmanned aerial vehicle Images. Agronomy, 13, 2088.

Yang H, Zhang Z, Zhang N, Li T, Wang J, Zhang Q, Xue J, Zhu W, Xu S. 2024. QTL mapping for plant height and ear height using bi-parental immortalized heterozygous populations in maize. Frontiers in Plant Science, 15, 1371394.

Yang J, Liu Z, Chen Q, Qu Y, Tang J, Lübberstedt T, Li H. 2020. Mapping of QTL for grain yield components based on a DH population in maize. Scientific Reports, 10, 7086.

Yang L, Li T, Liu B, Li R, Yu R, Zhang X, Li Q, Xu S, Xue J. 2021. Genetic analysis of ear-related traits under different pollination treatments in maize (Zea mays). Plant Breeding, 140, 211–222.

Yang W, Feng H, Zhang X, Zhang J, Doonan J H, Batchelor W D, Xiong L, Yan J. 2020. Crop phenomics and high-throughput phenotyping: Past decades, current challenges, and future perspectives. Molecular Plant, 13, 187–214.

Yang W, Guo Z, Huang C, Duan L, Chen G, Jiang N, Fang W, Feng H, Xie W, Lian X. 2014. Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nature Communications, 5, 5087.

Yang W, Zhang X, Duan L. 2021. High-throughput phenotyping (HTP) and genetic analysis technologies reveal the genetic architecture of grain crops. In: Zhou J, Nguyen H T, eds., High-Throughput Crop Phenotyping. Concepts and Strategies in Plant Sciences. Springer, Cham, Switzerland. pp. 101–127.

Yu D, Zha Y, Shi L, Jin X, Hu S, Yang Q, Huang K, Zeng W. 2020. Improvement of sugarcane yield estimation by assimilating UAV-derived plant height observations. European Journal of Agronomy, 121, 126159.

Yu T, Lei Y, Lu H, Zhang B, LI Y, Chen L, Ge T l, Liu Y l, Han J, LI Y. 2022. QTL analysis for plant height and fine mapping of two environmentally stable QTLs with major effects in soybean. Journal of Integrative Agriculture, 21, 933–946.

Yu X, Zhang M, Yu Z, Yang D, Li J, Wu G, Li J. 2020. An SNP-based high-density genetic linkage map for tetraploid potato using specific length amplified fragment sequencing (SLAF-Seq) technology. Agronomy, 10, 114.

Zhang X, Huang C, Wu D, Qiao F, Li W, Duan L, Wang K, Xiao Y, Chen G, Liu Q. 2017. High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth. Plant Physiology, 173, 1554–1564.

Zhou L, Gu X, Cheng S, Yang G, Shu M, Sun Q. 2020. Analysis of plant height changes of lodged maize using UAV-LiDAR data. Agriculture, 10, 146.

Zhou Z, Zhang C, Lu X, Wang L, Hao Z, Li M, Zhang D, Yong H, Zhu H, Weng J. 2018. Dissecting the genetic basis underlying combining ability of plant height related traits in maize. Frontiers in Plant Science, 9, 1117.

Zhou Z, Zhang C, Zhou Y, Hao Z, Wang Z, Zeng X, Di H, Li M, Zhang D, Yong H. 2016. Genetic dissection of maize plant architecture with an ultra-high density bin map based on recombinant inbred lines. BMC Genomics, 17, 1–15.