Peanut (Arachis hypogaea L.) is an important food and oilseed crop in the United States and worldwide with high net returns. However, input costs are high (US$1,970-$2,220 ha −1), and yield in excess of 4,500 kg ha −1 is needed to offset the costs. Since yield is limited by biotic and abiotic stresses, newer cultivars with tolerance to these stresses are needed to optimize yield. Plant height and canopy architecture may affect crop water use and plant disease resistance. However, measuring canopy height is a time-consuming process. Surface elevation models from red-green-blue (RGB) aerial images have been successfully developed to estimate genetic differences of plant height for tall crops like corn (Zea mays L.) and sorghum [Sorghum bicolor (L) Moench]; but they have not been tested for short crops like peanut with a runner growth habit and much smaller height differences among genotypes. The objective of this study was to derive canopy height of peanut from digital surface models (DSM). Images were aerially taken using a digital camera mounted on an unmanned aerial vehicle (UAV). Images were orthomosaiced to create the DSM and the digital terrain model (DTM) of the plot. Canopy height was derived by subtracting the DTM from the DSM in ArcGIS software. Results showed that the RGB derived canopy height was highly correlated to the manually measured height (R 2 = .953). We propose the methods used here as fast and relatively easy selection tools for breeders and crop growth evaluators of peanut plant height. 1 INFORMATION Peanut (Arachis hypogaea L.) is an important oil and food crop, grown on 17 million ha worldwide. In the United States, peanut is a high net return crop grown annually on Abbreviations: DSM, digital surface model; DTM, digital terrain model; GP, ground point; GSD, ground sample distance; RGB, red-green-blue; SfM, structure from motion; UAV, unmanned aerial vehicle; WAP, weeks after planting. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Leaf area index (LAI) is the ratio of the total one-sided leaf area to the ground area, whereas lateral growth (LG) is the measure of canopy expansion. They are indicators for light capture, plant growth, and yield. Although LAI and LG can be directly measured, this is time consuming. Healthy leaves absorb in the blue and red, and reflect in the green regions of the electromagnetic spectrum. Aerial high-throughput phenotyping (HTP) may enable rapid acquisition of LAI and LG from leaf reflectance in these regions. In this paper, we report novel models to estimate peanut (Arachis hypogaea L.) LAI and LG from vegetation indices (VIs) derived relatively fast and inexpensively from the red, green, and blue (RGB) leaf reflectance collected with an unmanned aerial vehicle (UAV). In addition, we evaluate the models’ suitability to identify phenotypic variation for LAI and LG and predict pod yield from early season estimated LAI and LG. The study included 18 peanut genotypes for model training in 2017, and 8 genotypes for model validation in 2019. The VIs included the blue green index (BGI), red-green ratio (RGR), normalized plant pigment ratio (NPPR), normalized green red difference index (NGRDI), normalized chlorophyll pigment index (NCPI), and plant pigment ratio (PPR). The models used multiple linear and artificial neural network (ANN) regression, and their predictive accuracy ranged from 84 to 97%, depending on the VIs combinations used in the models. The results concluded that the new models were time- and cost-effective for estimation of LAI and LG, and accessible for use in phenotypic selection of peanuts with desirable LAI, LG and pod yield.
Maintaining winter wheat (Triticum aestivum L.) productivity with more efficient nitrogen (N) management will enable growers to increase profitability and reduce the negative environmental impacts associated with nitrogen loss. Wheat breeders would therefore benefit greatly from the identification and application of genetic markers associated with nitrogen use efficiency (NUE). To investigate the genetics underlying N response, two bi-parental mapping populations were developed and grown in four site-seasons under low and high N rates. The populations were derived from a cross between previously identified high NUE parents (VA05W-151 and VA09W-52) and a shared common low NUE parent, 'Yorktown.' The Yorktown × VA05W-151 population was comprised of 136 recombinant inbred lines while the Yorktown × VA09W-52 population was comprised of 138 doubled haploids. Phenotypic data was collected on parental lines and their progeny for 11 N-related traits and genotypes were sequenced using a genotyping-by-sequencing platform to detect more than 3,100 high quality single nucleotide polymorphisms in each population. A total of 130 quantitative trait loci (QTL) were detected on 20 chromosomes, six of which were associated with NUE and N-related traits in multiple testing environments. Two of the six QTL for NUE were associated with known photoperiod (Ppd-D1 on chromosome 2D) and disease resistance (FHB-4A) genes, two were reported in previous investigations, and one QTL, QNue.151-1D, was novel. The NUE QTL on 1D, 6A, 7A, and 7D had LOD scores ranging from 2.63 to 8.33 and explained up to 18.1% of the phenotypic variation. The QTL identified in this study have potential for marker-assisted breeding for NUE traits in soft red winter wheat.
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