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.
Turfgrasses are measured aesthetically and by their ability to withstand stressors. Historically, researchers quantified acceptability by visual quality, but inconsistencies necessitate the use of vegetation indices (VIs) as an objective measurement. Indiscernible relationships have been established between turfgrass canopy normalized difference vegetation index (NDVI) and important variables such as soil moisture and leaf chlorophyll content. Alternative and variable–specific indices have been established in cropping systems. The water band index (WBI) is used for predicting water availability in cropping systems but has not been explored within turfgrass systems. The objective of this study was to compare the relationships of established VIs with chlorophyll content of ‘L‐93’ creeping bentgrass (Agrostis stolonifera L.) and water content of a sand‐based root zone maintained under greenhouse conditions. All VIs were moderately to strongly correlated to total chlorophyll content (r = 0.49–0.85). Turf color was more closely related to chlorophyll indices than to wet‐lab quantification. Only WBI (r ≥ 0.80) and the green/red ratio index (GRI; r ≥ 0.50) were consistently related to soil water content. The NDVI was weakly related to soil water content in one trial (r = 0.49). Nonlinear regression showed that WBI can be useful for estimating a decline in soil water content as water first becomes limiting for creeping bentgrass and may offer a viable means to assess water availability independently of non‐moisture‐related stresses and more accurately compared with previous indices. Future research will evaluate WBI on broader geospatial scales to assess practical application for turfgrass irrigation management.
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