ArticleFood chemistry: X2024
Machine learning-based non-destructive terahertz detection of seed quality in peanut.
Article in Food chemistry: X, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A lightweight YOLO-WRCS model for peanut pod detection in seed-preserving peanut shelling robots.Frontiers in plant science · 2026Article
- A comprehensive insight into peanut storage: patterns of quality changes, pathways of quality deterioration, and storage strategies.Food chemistry: X · 2025Review
- Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes.Foods (Basel, Switzerland) · 2025Review
- Leveraging sensor technologies for seed phenotyping by genebanks.Frontiers in plant science · 2025Article
- Rapid and accurate detection of peanut pod appearance quality based on lightweight and improved YOLOv5_SSE model.Frontiers in plant science · 2025Article
- THz image recognition of moldy wheat based on multi-scale context and feature pyramid.Frontiers in plant science · 2025Article
- Optimizing maize germination forecasts with random forest and data fusion techniques.PeerJ. Computer science · 2024Article
- Identifying Baicalin Concentration in Scutellaria Spray Drying Powder With Disturbed Terahertz Spectra Based on Gaussian Mixture Model.Journal of analytical methods in chemistry · 2024Article
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Authors and funding
8 authors.
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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.
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