Modern maize (Zea mays L.) hybrids are generally regarded as strongly population dependent because maximum grain yields (GYs) per area are achieved primarily in high-density populations. is study was conducted to analyze changes in density independence with plant density based on the response of GY, dry matter (DM) accumulation, and the harvest index (HI) to changes in plant density. Two modern cultivars, ZhengDan958 and ZhongDan909, were planted at 12 densities ranging from 1.5 to 18 plants m -2 . e experiment was conducted for 3 yr, with drip irrigation and plastic mulching, at the 71 Group and Qitai Farms located in Xinjiang, China. With increased plant density, DM accumulation per area increased logarithmically, the HI decreased according to a cubic curve, and GY per area increased quadratically; the optimum density was 10.57 plants m -2 . Further analysis showed that the response of GY per area, DM per area, and the HI to changes in plant density could be divided into four density ranges: Range I (£4.7 plants m -2 ), in which DM per area, the HI, and GY per area were signi cantly a ected by density; Range II (4.7-8.3 plants m -2 ), in which the HI was una ected by density but DM per area and GY per area were signi cantly a ected; Range III (8.3-10.75 plants m -2 ), in which GY per area was una ected by density but DM per area and the HI were signi cantly a ected; and Range IV (³10.7 plants m -2 ), in which DM per area was una ected by density but the HI and GY per area were signi cantly a ected. ese results indicated that Range II is a density-independent range and Range III is a GY-stable range.

This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. 2 0274-6638/20©2020IEEE IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE MONTH 2020 C rop 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. BACKGROUND Worldwide demand for food will increase through 2050 and beyond due to the increasing global human population. This represents a huge challenge to crop researchers and agricultural policymakers because current yield gain rates will not be sufficient for the demands of population growth, while climate change will make the difficulty even greater. Today's DNA sequencing, marker-assisted breeding, transgenic technology, genome-wide association study (GWAS) approaches, and quantitative trait loci (QTL) identification have been applied, to a limited extent, to improve crop yields [1]-[4]. While it is now relatively easy to select for monogenic traits, current genome sequence datasets have not been sufficiently mined for more genetically complex (multigenic) performance characteristics, at least in part because of the lack of crop phenotypic information collected from real-world field situations. Furthermore, traditional crop growth analysis often involves destructive sampling that is time-consuming and prone to measurement error. At High-Throughput Estimation of Crop Traits A review of ground and aerial phenotyping platforms

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