High‐throughput measurement of peanut canopy height using digital surface models

Abstract

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.

Exploration of Alternative Approaches to Phenotyping of Late Leaf Spot and Groundnut Rosette Virus Disease for Groundnut Breeding

“…Recent advances in plant phenotyping involve the use of unmanned aerial vehicles (UAVs) to collect several images generating large amounts of data. Several studies have reported that UAVs are faster and more effective for phenotyping large populations for traits such as height and drought tolerance in groundnut breeding ( Sarkar et al, 2020 , 2021 ) hence providing the desired high-throughput. This study therefore lays the foundation for investment in such more advanced equipment in groundnut breeding for selection for resistance to late leaf spot and groundnut rosette disease which are the most important foliar diseases in Uganda and SSA.…”

“…In groundnut breeding, application of HTP methods to complement or replace traditional phenotyping is in incipient stages. Efforts have been put forward to develop HTP methods to assess leaf wilting ( Sarkar et al, 2021 ), plant height ( Yuan et al, 2019 ; Sarkar et al, 2020 ), and plant population and variety differentiation using RGB and NDVI ( Oakes and Balota, 2017 ). No HTP methods are yet available for phenotyping LLS and GRD resistance in groundnut.…”

Optimizing Seeding Ratio for Semi-Leafless and Leafed Pea Mixture with Precise UAV Quantification of Crop Lodging

“…The canopy height by UAV image estimation provides high accuracy, with an r 2 = 0.88 and RMSE = 2.6 cm compared with ground measurement. Previous studies used UAV images and characterized the canopy height with structure-from-motion and the results showed an r 2 = 0.85-0.95 canopy height estimation in peanut, wheat, maize, and vineyard [18,20,21,36]. Through canopy height estimation in pre-and post-lodging, the previous study quantitatively assessed the lodging severity across 1320 and then identified a key genomic region for the underlying genetic architecture of lodging in wheat [36].…”

“…Previous studies successfully used RGB, multispectral, hyperspectral, and Lidar camera in UAV-imaging to measure crop height [15][16][17][18][19]. Previous studies have shown that canopy heights derived from UAV images had a strong correlation (r 2 > 0.9) with the ground measurements [20,21]. As such, it should be possible to assess lodging severity by computing a time series of canopy height from UAV flights, whereby determining the most lodging resistant mixture.…”

The Use of Very-High-Resolution Aerial Imagery to Estimate the Structure and Distribution of the Rhanterium epapposum Community for Long-Term Monitoring in Desert Ecosystems

“…A primary step in estimating maximum shrub height is to subtract the DSM from the DTM [ 68 ]. The DSM layer was generated on the basis of the densified 3D point cloud using Pix4Dmapper software, representing the height of all image objects, including shrubs, fences, buildings, rocks, and the ground.…”