Phenotyping a diversity panel of quinoa using UAV-retrieved leaf area index, SPAD-based chlorophyll and a random forest approach

Jiale Jiang,Khalid A Asiry,Gabriele M Fiene,Magdi A A Mousa,Matthew F Mccabe,Clara S Stanschewski,Gordon Wellman,Kasper Johansen,Mark Tester

doi:10.1007/s11119-021-09870-3

Abstract

Given its high nutritional value and capacity to grow in harsh environments, quinoa has significant potential to address a range of food security concerns. Monitoring the development of phenotypic traits during field trials can provide insights into the varieties best suited to specific environmental conditions and management strategies. Unmanned aerial vehicles (UAVs) provide a promising means for phenotyping and offer the potential for new insights into relative plant performance. During a field trial exploring 141 quinoa accessions, a UAV-based multispectral camera was deployed to retrieve leaf area index (LAI) and SPAD-based chlorophyll across 378 control and 378 saline-irrigated plots using a random forest regression approach based on both individual spectral bands and 25 different vegetation indices (VIs) derived from the multispectral imagery. Results show that most VIs had stronger correlation with the LAI and SPAD-based chlorophyll measurements than individual bands. VIs including the red-edge band had high importance in SPAD-based chlorophyll predictions, while VIs including the near infrared band (but not the red-edge band) improved LAI prediction models. When applied to individual treatments (i.e. control or saline), the models trained using all data (i.e. both control and saline data) achieved high mapping accuracies for LAI (R2 = 0.977–0.980, RMSE = 0.119–0.167) and SPAD-based chlorophyll (R2 = 0.983–0.986, RMSE = 2.535–2.861). Overall, the study demonstrated that UAV-based remote sensing is not only useful for retrieving important phenotypic traits of quinoa, but that machine learning models trained on all available measurements can provide robust predictions for abiotic stress experiments.

Highlights

Chenopodium quinoa Willd is a highly nutritious crop that can provide an excellent balance of fiber, lipids, carbohydrates, vitamins, and minerals (Vega‐Gálvez et al, 2010)
The importance of different variables for estimation of leaf area index (LAI) and soil–plant analysis development (SPAD)-based chlorophyll using random forest regression was evaluated, with results presented in Figs. 2 and 3
The results illustrate that vegetation indices (VIs) were generally more important than the individual spectral bands for predicting LAI and SPAD-based chlorophyll, which is in line with the findings of previous studies using VIs to mitigate the effects of illumination geometry and enhance the spectral information of crops (Fernandez-Gallego et al, 2019; Lillesand et al, 2015; Xue & Su, 2017)

Summary

Introduction

Chenopodium quinoa Willd (quinoa) is a highly nutritious crop that can provide an excellent balance of fiber, lipids, carbohydrates, vitamins, and minerals (Vega‐Gálvez et al, 2010). To improve agronomic traits and increase the productivity of quinoa, ongoing monitoring of phenotypic traits of different varieties and under a range of field conditions are required to help identify the suitable accessions for cultivation. In recent years, unmanned aerial vehicles (UAVs) have become an increasingly useful tool for the collection of ultra-high spatial resolution, near real-time sensor-agnostic information (Manfreda et al, 2018; Xiang et al, 2019). While they have been increasingly employed for a variety of plant-phenotyping and crop breeding type applications, to date, relatively few studies have assessed their potential for mapping quinoa phenotypic traits. Apart from the two above-mentioned studies, no other research that uses UAV-based data for predicting quinoa phenotypic traits has been identified

Objectives

Methods

Results

Discussion

Conclusion