High‐Throughput Field Phenotyping to Assess Irrigation Treatment Effects in Quinoa

Abstract

Core Ideas Water stress in multiple quinoa varieties were introduced. Remote sensing technologies were evaluated to assess water stress response. Handheld and unmanned aerial system based sensors provided reliable data. Thermal imaging data could capture stress in quinoa varieties. Quinoa ( Chenopodium quinoa Willd.) is a crop known for its tolerance to abiotic stress such as drought and salinity. Quinoa is also a versatile superfood, which is gluten‐free and high in protein, vitamins, nutrients, and beneficial antioxidants. Washington State University's quinoa breeding program efforts focus on identification and development of varieties that are resilient to local environmental conditions. In this study, high‐throughput phenotyping techniques were applied to evaluate the performance of quinoa varieties under different irrigation regimes. Handheld multispectral radiometer (Crop Scan), proximal sensing system using ground platform, and remote sensing with unmanned aerial system (UAS) were used to assess the performance of eight quinoa varieties under two irrigation treatments (non‐irrigated and irrigated). Crop Scan data, along with multispectral and thermal infrared images, were acquired at multiple time points at different stages of crop development during the season. In general, the normalized difference vegetation index, water band index, and green normalized difference vegetation index (GNDVI) data extracted from Crop Scan, and GNDVI and canopy temperature data extracted from UAS were able to detect irrigation treatment effects. Comparing the spectral data acquired at multiple scales indicated that Crop Scan data were highly and significantly correlated with remote sensing data (| r | = 0.57–0.85). Rapid data acquisition and the ability to detect differences among varieties under water stress highlight the application of remote sensing techniques as a high‐throughput phenotyping tool to evaluate quinoa.

Plant phenotyping relevance

複数のリモートセンシング手法を用いた植物ストレス形質の高スループット取得・相関評価が研究の中心であり、単なる灌漑試験の routine 測定ではない。

Code and data availability

The article describes quinoa phenotyping with Crop Scan, proximal, and UAS multispectral/thermal sensing, referencing supplemental tables/figures and custom Matlab/R analysis, but no public dataset, image, code, or model deposit with an authors' URL is mentioned anywhere in the supplied blocks.

No evidence-backed public reproduction asset is currently recorded.