Digital techniques and trends for seed phenotyping using optical sensors.

Area of Science:

• Agricultural Science
• Biotechnology
• Data Science

Background:

• Seed phenotyping is crucial for developing high-quality, high-yield, disease-resistant crop varieties essential for food security.
• Digitalization and optical sensor technologies offer non-contact, high-throughput methods for seed phenotype evaluation, enhancing breeding efficiency.

Purpose of the Study:

• To review principles, methods, and challenges of spectroscopy, digital imaging, and 3D reconstruction for seed phenotyping.
• To investigate the application of optical sensor-based digital techniques for maize seed traits (external visible phenotype and internal invisible phenotype).
• To discuss future trends in equipment, data processing, and algorithms for seed phenotyping.

Main Methods:

• Review of spectroscopy, digital imaging, and 3D reconstruction principles and applications.
• Analysis of optical sensor data processing techniques for seed phenotype traits.
• Investigation of external visible phenotype (EVP) and internal invisible phenotype (IIP) digitization.

Main Results:

• Optical sensor-based digital techniques facilitate non-contact, high-throughput seed phenotype evaluation.
• Tailoring digital techniques to specific seed traits minimizes resource loss and improves decision-making.
• Future advancements will drive more automated, integrated, and intelligent seed phenotyping.

Conclusions:

• Digital techniques based on optical sensors are vital for efficient seed evaluation and crop breeding.
• Strategic selection of techniques enhances resource management and breeding outcomes.
• Continued innovation in technology and algorithms will bridge the gap between digital tools and seed phenotyping needs.