Crop Seed Phenomics: Focus on Non-Destructive Functional Trait Phenotyping Methods and Applications

Seeds play a critical role in ensuring food security for the earth's 8 billion people. There is great biodiversity in plant seed content traits worldwide. Consequently, the development of robust, rapid, and high-throughput methods is required for seed quality evaluation and acceleration of crop improvement. There has been considerable progress in the past 20 years in various non-destructive methods to uncover and understand plant seed phenomics. This review highlights recent advances in non-destructive seed phenomics techniques, including Fourier Transform near infrared (FT-NIR), Dispersive-Diode Array (DA-NIR), Single-Kernel (SKNIR), Micro-Electromechanical Systems (MEMS-NIR) spectroscopy, Hyperspectral Imaging (HSI), and Micro-Computed Tomography Imaging (micro-CT). The potential applications of NIR spectroscopy are expected to continue to rise as more seed researchers, breeders, and growers successfully adopt it as a powerful non-destructive method for seed quality phenomics. It will also discuss the advantages and limitations that need to be solved for each technique and how each method could help breeders and industry with trait identification, measurement, classification, and screening or sorting of seed nutritive traits. Finally, this review will focus on the future outlook for promoting and accelerating crop improvement and sustainability.

Automated summary

Seeds are crucial for ensuring food security for the earth's population of 8 billion people and there is a wide range of biodiversity in plant seed traits globally. Non-destructive seed phenomics techniques, including NIR spectroscopy, HSI, and micro-CT, have made significant advancements in uncovering and understanding plant seed characteristics. The potential applications of NIR spectroscopy are expected to continue rising as a powerful method for seed quality evaluation. This review also discusses the advantages and limitations of each technique and their potential for assisting breeders and industry in seed trait identification and classification.