Advances in optical phenotyping of cereal crops. (February 2022)

• Record Type: Journal Article
• Authors: Sun, Dawei; Robbins, Kelly; Morales, Nicolas; Shu, Qingyao; Cen, Haiyan
• Is Part Of: Trends in plant science. Volume 27:Number 2(2022)
• Page Start: 191
• Page End: 208
• Publication Date: 2022-02
• Subjects: cereal crops -- high-throughput phenotyping -- optical sensors -- traits; Botany -- Periodicals; Botanique -- Périodiques; Botany; Periodicals; 580.5
• DOI: 10.1016/j.tplants.2021.07.015
• Languages: English
• ISSNs: 1360-1385

Abstract

Abstract : Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories – morphological, biochemical, physiological, and performance traits – and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established. Highlights: Full understanding of principles of sensors and sensing techniques is crucial for calibration and standardization among existing OSP platforms in overcoming crop breeding bottlenecks. OSP can extract morphological, biochemical, physiological, and performance traits of cereal crops for accelerated breeding. Efficient data-processing solutions to overcome the inherent limitations of phenotypic big data will be necessary to mine knowledge and eventually bridge the gap between phenotypic data and genetic variations. The advantages of optical sensing-based traits, combined with standardization of the language of phenotyping and collaboration between researchers worldwide, will continue to improve the performance and applicability of OSP.