Machine learning-based non-destructive terahertz detection of seed quality in peanut.

Abstract

Rapid identification of peanut seed quality is crucial for public health. In this study, we present a terahertz wave imaging system using a convolutional neural network (CNN) machine learning approach. Terahertz waves are capable of penetrating the seed shell to identify the quality of peanuts without causing any damage to the seeds. The specificity of seed quality on terahertz wave images is investigated, and the image characteristics of five different qualities are summarized. Terahertz wave images are digitized and used for training and testing of convolutional neural networks, resulting in a high model accuracy of 98.7% in quality identification. The trained THz-CNNs system can accurately identify standard, mildewed, defective, dried and germinated seeds, with an average detection time of 2.2 s. This process does not require any sample preparation steps such as concentration or culture. Our method swiftly and accurately assesses shelled seed quality non-destructively.

Plant phenotyping relevance

落花生種子の品質・カビ・欠損・乾燥・発芽状態を、テラヘルツ画像とCNNで非破壊的に識別する手法が研究の中心であり、種子状態という植物表現型を抽出している。

Code and data availability

The paper's terahertz seed images, dataset, and trained CNN model are not publicly deposited; the authors state they are available upon request from the first author.

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