Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

Abstract

Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.

The incompletely fulfilled promise of embryo transfer in cattle—why aren’t pregnancy rates greater and what can we do about it?

“…The phenome is the complete phenotypic representation of a species ( Mahner and Kary, 1997 ), and phenomics is the study of the phenome ( Freimer and Sabatti, 2003 ). Phenomics is being emphasized as an important tool for the management and genetic improvement of crops ( Mochida et al, 2020 ; Yang et al, 2020 ) and livestock ( Koltes et al, 2019 ). As the preceding paragraphs make apparent, there is a large collection of phenotypes describing the ability of both the embryo to establish pregnancy and the female to support that pregnancy.…”

High‐throughput phenotyping accelerates the dissection of the dynamic genetic architecture of plant growth and yield improvement in rapeseed

“…However, scoring of the selected i-traits in the field might pose some challenges, for example due to the overlap of plants caused by high planting density, which could be solved by 3D imaging (Guo et al, 2018), and a robust identification model (deep learning, Singh et al, 2018). Other technical challenges using UAV include the variable wind speed, the effect of solar radiation, reasonable flight height and so on (Yang, et al, 2020), which should be further investigated. 3.…”

Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning

“…To some degree, the essence of crop sciences is the knowledge of selection (by breeders) or regulation (by agronomists) of agronomical traits. Traditionally, crop scientists heavily depend on visual inspection of crops in the eld as well as their evaluation of target traits based on their experience and expertise of the crop, which is labor intensive, time consuming, relatively subjective, and prone to errors [14,47]. In addition, the target traits are mainly morphological traits including leaf senescence, plant height, tillering capacity, panicle or spike size, and growth periods, while fewer physiological traits are monitored and analyzed.…”