Research on Non-Destructive Quality Detection of Sunflower Seeds Based on Terahertz Imaging Technology.

Abstract

The variety and content of high-quality proteins in sunflower seeds are higher than those in other cereals. However, sunflower seeds can suffer from abnormalities, such as breakage and deformity, during planting and harvesting, which hinder the development of the sunflower seed industry. Traditional methods such as manual sensory and machine sorting are highly subjective and cannot detect the internal characteristics of sunflower seeds. The development of spectral imaging technology has facilitated the application of terahertz waves in the quality inspection of sunflower seeds, owing to its advantages of non-destructive penetration and fast imaging. This paper proposes a novel terahertz image classification model, MobileViT-E, which is trained and validated on a self-constructed dataset of sunflower seeds. The results show that the overall recognition accuracy of the proposed model can reach 96.30%, which is 4.85%, 3%, 7.84% and 1.86% higher than those of the ResNet-50, EfficientNeT, MobileOne and MobileViT models, respectively. At the same time, the performance indices such as the recognition accuracy, the recall and the F1-score values are also effectively improved. Therefore, the MobileViT-E model proposed in this study can improve the classification and identification of normal, damaged and deformed sunflower seeds, and provide technical support for the non-destructive detection of sunflower seed quality.

Plant phenotyping relevance

ヒマワリ種子の正常・損傷・変形状態をテラヘルツ画像から非破壊分類するモデルを開発・検証しており、植物器官の状態取得が研究の中心である。

Code and data availability

The supplied blocks describe a self-constructed terahertz image dataset of sunflower seeds (2340 images) and a MobileViT-E model, but contain no data or code availability statement, no public repository, and no author-provided URL for the dataset, images, or trained model. No paper-specific public asset is available.

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