ArticleSensors (Basel, Switzerland)2023
Semisupervised Deep Learning for the Detection of Foreign Materials on Poultry Meat with Near-Infrared Hyperspectral Imaging.
Article in Sensors (Basel, Switzerland), 2023. 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, 20 citations in OpenAlex.
- Artificial intelligence in poultry processing: applications, validation gaps, and pathways toward intelligent and autonomous processing systems.Poultry science · 2026Review
- Hyperspectral Imaging Combined with Multi-Scale Feature Fusion and Improved Faster R-CNN for Automated Detection of Surface Defects on Chicken Carcasses.Foods (Basel, Switzerland) · 2026Article
- AI-Driven Nondestructive Measurement Technologies for Meat Quality and Safety: A Review.Foods (Basel, Switzerland) · 2026Review
- From machine learning to digital twin integration for livestock production and research.Frontiers in veterinary science · 2026Review
- Recent Advances in Spectroscopy and Imaging Techniques for Nondestructive Detection of Meat Quality and Safety.Food science & nutrition · 2025Review
- Hyperspectral Imaging for Identifying Foreign Objects on Pork Belly.Sensors (Basel, Switzerland) · 2025Article
- Innovative Technologies Reshaping Meat Industrialization: Challenges and Opportunities in the Intelligent Era.Foods (Basel, Switzerland) · 2025Review
- Deep Learning Model Compression and Hardware Acceleration for High-Performance Foreign Material Detection on Poultry Meat Using NIR Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A novel semisupervised hyperspectral imaging technique was developed to detect foreign materials (FMs) on raw poultry meat. Combining hyperspectral imaging and deep learning has shown promise in identifying food safety and quality attributes. However, the challenge lies in acquiring a large amount of accurately annotated/labeled data for model training. This paper proposes a novel semisupervised hyperspectral deep learning model based on a generative adversarial network, utilizing an improved 1D U-Net as its discriminator, to detect FMs on raw chicken breast fillets. The model was trained by using approximately 879,000 spectral responses from hyperspectral images of clean chicken breast fillets in the near-infrared wavelength range of 1000-1700 nm. Testing involved 30 different types of FMs commonly found in processing plants, prepared in two nominal sizes: 2 × 2 mm
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.