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

文献类型： 外文期刊

第一作者： Jin, Chen

作者： Jin, Chen;Zhang, Chu;Qi, Hengnian;Zhou, Lei;Pu, Yuanyuan;Zhao, Yiying

作者机构：

关键词： Seed phenotypes; Computer vision; Hyperspectral imaging; Convolutional neural networks; Transfer learning; Multi-dimensional data

期刊名称：ARTIFICIAL INTELLIGENCE REVIEW （ 影响因子：13.9； 五年影响因子：14.9 ）

ISSN： 0269-2821

年卷期： 2025 年 58 卷 3 期

页码：

收录情况： SCI

摘要： 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.

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