Digital techniques and trends for seed phenotyping using optical sensors

Fei Liu; Rui Yang; Rongqin Chen; Mahamed Lamine Guindo; Yong He; Jun Zhou; Xiangyu Lu; Mengyuan Chen; Yinhui Yang; Wenwen Kong

doi:10.1016/j.jare.2023.11.010

Digital techniques and trends for seed phenotyping using optical sensors

J Adv Res. 2023 Nov 11:S2090-1232(23)00347-8. doi: 10.1016/j.jare.2023.11.010. Online ahead of print.

Authors

Fei Liu¹, Rui Yang², Rongqin Chen², Mahamed Lamine Guindo², Yong He², Jun Zhou³, Xiangyu Lu², Mengyuan Chen², Yinhui Yang⁴, Wenwen Kong⁵

Affiliations

¹ College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. Electronic address: fliu@zju.edu.cn.
² College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
³ College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
⁴ College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China.
⁵ College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China. Electronic address: wwkong16@zafu.edu.cn.

PMID: 37956859
DOI: 10.1016/j.jare.2023.11.010

Abstract

Background: The breeding of high-quality, high-yield, and disease-resistant varieties is closely related to food security. The investigation of breeding results relies on the evaluation of seed phenotype, which is a key step in the process of breeding. In the global digitalization trend, digital technology based on optical sensors can perform the digitization of seed phenotype in a non-contact, high throughput way, thus significantly improving breeding efficiency.

Aim of review: This paper provides a comprehensive overview of the principles, characteristics, data processing methods, and bottlenecks associated with three digital technique types based on optical sensors: spectroscopy, digital imaging, and three-dimensional (3D) reconstruction techniques. In addition, the applicability and adaptability of digital techniques based on the optical sensors of maize seed phenotype traits, namely external visible phenotype (EVP) and internal invisible phenotype (IIP), are investigated. Furthermore, trends in future equipment, platform, phenotype data, and processing algorithms are discussed. This review offers conceptual and practical support for seed phenotype digitization based on optical sensors, which will provide reference and guidance for future research.

Key scientific concepts of review: The digital techniques based on optical sensors can perform non-contact and high-throughput seed phenotype evaluation. Due to the distinct characteristics of optical sensors, matching suitable digital techniques according to seed phenotype traits can greatly reduce resource loss, and promote the efficiency of seed evaluation as well as breeding decision-making. Future research in phenotype equipment and platform, phenotype data, and processing algorithms will make digital techniques better meet the demands of seed phenotype evaluation, and promote automatic, integrated, and intelligent evaluation of seed phenotype, further helping to lessen the gap between digital techniques and seed phenotyping.

Keywords: Artificial intelligence; Digital technique; Imaging processing; Optical sensor; Seed phenotype; Spectroscopy.

Publication types

Review