Translating High-Throughput Phenotyping into Genetic Gain

Abstract

Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and 'phenotypers' in an effective manner.

Keywords: field phenotyping; genetic gain; high-throughput; remote sensing.

Figure 1

Key Figure: Five Pillars of Increasing Genetic Gain in Breeding Programs (Adapted from [20]) High-throughput phenotyping contributes directly to three of the pillars, increasing selection accuracy by increasing heritability (H) through a priori or a posteriori control of spatial variation and improved disease phenotyping helping to identify the genetic variation available in a more efficient manner, and making the decision support systems more robust. By contrast, proper phenotyping contributes indirectly to optimization of these five components. Examples of high-throughput phenotyping tools are provided at the bottom. TPEs, target populations of environments, where the products of the breeding programs would be grown.

Figure 2

Summary of the Different Remote-Sensing Tools Most Commonly Used to Assess Shoot Characteristics of the Crop under Field Conditions, Together with a Comparative List of the Potential Applications and Their Level of Technological Development and Adoption. Different radar options are not included.

Figure I

Examples of Potential Applications of Field Phenotyping with Red–Green–Blue (RGB) Images Produced by Conventional Digital Cameras. Different categories of traits are included: counting crop characteristics, monitoring plant/crop growth and development, and three-dimensional reconstructions. Part of the figure is redrawn from Fred Baret, INRA (EWG on Wheat Phenotyping to support Wheat Improvement).

Figure I

Different Categories of Potential and Actual Ground and Aerial Phenotyping Platforms, along with the Spectral Ranges Used for Different Remote-Sensing Tools. RGB cameras (VIS), multispectral and hyperspectral sensors and cameras, light detection and ranging (LiDAR) sensors, thermal sensors and cameras (TIR/LWIR), and the different categories of Radars. The horizontal axis is partially redrawn from . The ground stationary platforms correspond to the Maricopa Agricultural Center (USA) and the ETH field phenotyping platform (Switzerland). The images of the phenomobile correspond to (from left to right) a proximal remote sensing buggy , , the phenomobile lite (https://www.youtube.com/watch?v=o8DmF7Y-GpE), and a phenocart . The unmanned helicopter is from Chapman et al.. IR, infrared; LWIR, long-wave infrared; NIR, near-infrared; SWIR, short-wave infrared; TIR, thermal infrared; VIS, visible.