Application of deep learning for high-throughput phenotyping of seed: a review

Abstract

Abstract Seed quality is of great importance for agricultural cultivation. High-throughput phenotyping techniques can collect magnificent seed information in a rapid and non-destructive manner. Emerging deep learning technology brings new opportunities for effectively processing massive and diverse data from seeds and evaluating their quality. This article comprehensively reviews the principle of several high-throughput phenotyping techniques for non-destructively collection of seed information. In addition, recent research studies on the application of deep learning-based approaches for seed quality inspection are reviewed and summarized, including variety classification and grading, seed damage detection, components prediction, seed cleanliness, vitality assessment, etc. This review illustrates that the combination of deep learning and high-throughput phenotyping techniques can be a promising tool for collection of various phenotype information of seeds, which can be used for effective evaluation of seed quality in industrial practical applications, such as seed breeding, seed quality inspection and management, and seed selection as a food source.

Plant phenotyping relevance

種子の高スループット表現型取得と深層学習による品質・形質評価を中心に扱う方法レビューであり、植物フェノタイピング手法のレビューとして適格です。

Code and data availability

This is a review article on deep learning for seed phenotyping. The authors explicitly state no datasets were generated or analysed, and no paper-specific datasets, images, code, or models are made available. All cited studies are prior work, not assets of this paper.

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