Aerial high-throughput phenotyping of peanut leaf area index and lateral growth

Sayantan Sarkar,Maria Balota,Joseph Oakes,David Mccall,Wade Thomason,Lynn Abbott,Alexandre-Brice Cazenave

doi:10.1038/s41598-021-00936-w

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.

Highlights

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
The models developed in this work were based on vegetation indices (VIs) derived from RGB images collected by an unmanned aerial vehicle (UAV) flown at 20 m above a peanut canopy early in the growing season, from 30 to 75 days after planting (DAP)
Previous studies have shown that resolution of aerial imagery from 20 m is suitable and does not cause significant changes to reflectance values when compared to proximal images taken at 1.2 m85

Summary

Introduction

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. 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). Abbreviations ANN Artificial neural network regression DAP Days after planting LAI Leaf area index LG Lateral growth RGB Red: Green: Blue UAV Unmanned aerial vehicle VIs Vegetation indices. Studies on peanut showed that biomass reduction, i.e. reduced leaf number and area by drought stress, resulted in significant pod yield decreas[0 7,8,9,10,11,12]. That variations in LG, caused by differences in lateral branching pattern, impacted flowering, pegging and pod formation, pod maturation, agronomic and disease management, and pod yield[13,14,16,17,18]

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Conclusion