Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

Abstract

High‐throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportunities from this technology for plant breeding are similar to the emergence of genomic tools ∼30 years ago, and genomic selection more recently. Unlike genomic tools, HTP provides a variety of strategies in implementation and utilization that generate big data on the dynamic nature of plant growth formed by temporal interactions between growth and environment. This review lays out strategies deployed across four major staple crop species: cotton (Gossypium hirsutum L.), maize (Zea mays L.), soybean (Glycine max L.), and wheat (Triticum aestivum L.). Each crop highlighted in this review demonstrates how UAS‐collected data are employed to automate and improve estimation or prediction of objective phenotypic traits. Each crop section includes four major topics: (a) phenotyping of routine traits, (b) phenotyping of previously infeasible traits, (c) sample cases of UAS application in breeding, and (d) implementation of phenotypic and phenomic prediction and selection. While phenotyping of routine agronomic and productivity traits brings advantages in time and resource optimization, the most potentially beneficial application of UAS data is in collecting traits that were previously difficult or impossible to quantify, improving selection efficiency of important phenotypes. In brief, UAS sensor technology can be used for measuring abiotic stress, biotic stress, crop growth and development, as well as productivity. These applications and the potential implementation of machine learning strategies allow for improved prediction, selection, and efficiency within breeding programs, making UAS HTP a potentially indispensable asset.

Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations

“…Unlike in precision agriculture studies, we masked the soil in calculating NGRDI so that it is not confounded with plant biomass or canopy coverage. Integrating temporal field‐based high‐throughput phenotyping with temporal modeling approaches such as FPCA, Gaussian peak models, and mechanistic growth models, combined with the association of temporal phenotypes in genetic mapping can unravel the complex intricacies of plant development (Guo et al ., 2021; Herr et al ., 2023).…”

“…Field‐based high‐throughput phenotyping (FHTP) combines cutting‐edge remote sensing technologies with traditional breeding and genetics approaches to rapidly and non‐invasively assess and quantify a wide range of plant traits in natural field conditions (Araus & Cairns, 2014). Field‐based high‐throughput phenotyping has gained significant attention and prominence in recent years due to its potential to accelerate crop improvement and enhance our understanding of plant responses to various environmental stresses (Pauli et al ., 2016; Shi et al ., 2016; Araus et al ., 2018; Wang et al ., 2019, 2021; Anderson et al ., 2020; Zhou et al ., 2021; Adak et al ., 2021a,b, 2023b; Oehme et al ., 2022; Sun et al ., 2022; Herr et al ., 2023; Li et al ., 2023). Field‐based high‐throughput phenotyping involves the integration of advanced sensing technologies, robotics, data analytics, and remote sensing with traditional agronomic practices.…”

“…Field‐based high‐throughput phenotyping involves the integration of advanced sensing technologies, robotics, data analytics, and remote sensing with traditional agronomic practices. These technologies include but are not limited to hyperspectral imaging, Light Detection and Ranging (LIDAR), thermal imaging, unmanned aerial vehicles (UAVs), and ground‐based robots equipped with various sensors (Guo et al ., 2021; Herr et al ., 2023). The primary goal of using these FHTP remote sensing technologies is to collect FHTP data across the crop growth cycle, and analyze it using machine learning or other statistical methods to extract valuable knowledge of plant performance within environments for decision‐making.…”

Remote sensing continuity: a comparison of HTP platforms and potential challenges with field applications

“…The vegetation index used depends on the crop, growth stage, and target trait. These factors influence reflectance values and relative index effectiveness ( Wientjes et al., 2017 ; Lozada et al., 2020 ; Herr et al., 2023 ). Vegetation indices have many applications in capturing routine trait estimates like plot quality, biotic, and abiotic stress ( Sankaran et al., 2015a ; Guo et al., 2021 ; Sarkar et al., 2022 ; Sapkota et al., 2023 ), as well as previously infeasible traits like chlorophyll content and nitrogen content ( Xie and Yang, 2020 ; Yin et al., 2022 ).…”

“…It has also been shown that SRI data can be utilized to improve tools like genomic selection for grain yield. Thus, grain yield is an appealing trait for a breeder to focus on when implementing HTP approaches ( Reynolds et al., 2020 ; Montesinos López et al., 2022 ; Herr et al., 2023 ).…”