High‐throughput phenotyping platforms for pulse crop biofortification

Abstract

Societal Impact Statement Pulse crops, including dry pea, lentil, and chickpea, are rich sources of protein, low digestible carbohydrates, and micronutrients. With the increasing demand for plant‐based protein with gluten‐free and allergen‐free foods, pulse crops have become of global importance for meeting the nutritional demand of growing populations. Breeding for nutritional quality is becoming a bottleneck for most breeding programs globally due to the cost of these available tools. Therefore, low‐cost, high‐throughput phenotyping tools will be a focus of interest for the selection of elite germplasm for cultivar development and gene identification for pulse cultivar development. This publication explains the emerging and future trends of phenotyping tools that are feasible for pulse breeding and improving nutritional quality. Summary Precision agriculture tools based on spectroscopic and imaging techniques now contribute to high‐throughput phenotyping (HTP) pipelines for nutritional and agronomic traits to speed breeding and selection for cultivar development. Fourier transform mid‐infrared (FT‐MIR) spectroscopy has been a reliable HTP tool for macro nutritional traits in pulse crops. Hyperspectral, multispectral, and RGB (red‐green‐blue) imaging with unmanned aerial systems (UAVs) have been developed to measure agronomic traits for cereals, but these techniques have yet to be developed and validated for pulse crops. This review summarizes different phenotyping techniques applied to nutritional and agronomic traits for crop breeding and reviews applications of machine learning tools for optimizing HTP.

Plant phenotyping relevance

パルス作物の栄養・農業形質を測定する高スループット表現型解析技術を中心に、分光・画像・機械学習手法をレビューしているため。

Code and data availability

This is a review article on high-throughput phenotyping platforms for pulse crop biofortification. The authors explicitly state no new data were created or analyzed, and no paper-specific datasets, images, code, models, or supplements are referenced. All URLs in the blocks are cited prior work, generic tools, or web/FA

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