Aerial high-throughput phenotyping of peanut leaf area index and lateral growth
| dc.contributor.author | Sarkar, Sayantan | en |
| dc.contributor.author | Cazenave, Alexandre-Brice | en |
| dc.contributor.author | Oakes, Joseph C. | en |
| dc.contributor.author | McCall, David S. | en |
| dc.contributor.author | Thomason, Wade E. | en |
| dc.contributor.author | Abbott, A. Lynn | en |
| dc.contributor.author | Balota, Maria | en |
| dc.date.accessioned | 2021-11-15T13:22:42Z | en |
| dc.date.available | 2021-11-15T13:22:42Z | en |
| dc.date.issued | 2021-11-04 | en |
| dc.description.abstract | 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. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | https://doi.org/10.1038/s41598-021-00936-w | en |
| dc.identifier.orcid | Oakes, Joseph [0000-0001-5931-4993] | en |
| dc.identifier.orcid | McCall, David [0000-0002-7113-9486] | en |
| dc.identifier.orcid | Abbott, Amos [0000-0003-3850-6771] | en |
| dc.identifier.orcid | Balota, Maria [0000-0003-4626-0193] | en |
| dc.identifier.uri | http://hdl.handle.net/10919/106650 | en |
| dc.identifier.volume | 11 | en |
| dc.language.iso | en | en |
| dc.publisher | Springer Nature | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | High-throughput screening | en |
| dc.subject | image processing | en |
| dc.subject | Machine learning | en |
| dc.subject | statistical methods | en |
| dc.title | Aerial high-throughput phenotyping of peanut leaf area index and lateral growth | en |
| dc.title.serial | Scientific Reports | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |