AIMC Topic: Hyperspectral Imaging

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Harnessing hyperspectral imaging and machine learning techniques for accurate discrimination of peanut plants and weeds.

Scientific reports
Effective weed detection for precise management remains a pertinent issue in modern agriculture. In this study, hyperspectral imaging (HSI) was combined with machine learning (ML) to differentiate between peanut plants and four common weeds found in ...

Research on the detection of foreign materials in tobacco shreds based on hyperspectral reflection imaging technology combined with machine learning.

Scientific reports
Plastic and paper foreign materials in tobacco shreds mainly originate from tobacco processing and packaging. These materials are highly similar to tobacco shreds in color and size, making them difficult for traditional machine vision systems to dete...

A new approach improving koala habitat prediction using hyperspectral airborne imagery.

The Science of the total environment
Koala populations are declining primarily due to habitat loss, making large-scale habitat quality prediction essential for conservation. A first approach to defining koala habitat quality involves identifying the number of different 'koala' trees spe...

Decoding the spectrum of meat quality: advances in hyperspectral imaging for multi-attribute analysis.

Food chemistry
Hyperspectral imaging (HSI) has emerged as a powerful non-destructive technique for evaluating fresh meat quality across multiple attributes simultaneously. This review critically examines recent advances in HSI applications for fresh beef, pork, and...

A multi-task deep learning model based on transformer for simultaneously evaluating the TVB-N and TVC contents of chicken breasts using two different hyperspectral imaging.

Food chemistry
Accurate assessment of freshness is crucial for ensuring quality and safety in the chicken meat industry. This study developed a Multi-task Interleaved Group Transformer Model (MIGTM) integrating dual hyperspectral imaging (HSI) data to simultaneousl...

Artificial neural networks as a prognostic tool using hyperspectral imaging on pretherapeutic histopathological specimens of esophageal adenocarcinoma.

Journal of cancer research and clinical oncology
PURPOSE: The integration of artificial intelligence (AI) with hyperspectral imaging (HSI) offers a promising avenue for improving pre-therapeutic prognosis, a key factor in optimizing cancer treatment strategies. This study explores the potential of ...

Exploring the role of preprocessing combinations in hyperspectral imaging for deep learning colorectal cancer detection.

Scientific reports
This study compares various preprocessing techniques for hyperspectral deep learning-based cancer diagnostics. The study considers different spectrum scaling and noise reduction options across spatial and spectral axes of hyperspectral datacubes, as ...

Hyperspectral anomaly detection leveraging spatial attention and right-shifted spectral energy.

PloS one
In this research, we have proposed a novel anomaly detection algorithm for processing hyperspectral images (HSIs), called the Graph Attention Network-Beta Wavelet Graph Neural Network-based Hyperspectral Anomaly Detection (GAN-BWGNN HAD). This algori...

Label-free classification of nanoscale drug delivery systems using hyperspectral imaging and convolutional neural networks.

International journal of pharmaceutics
Label-free characterization of nanoscale drug delivery systems remains a critical challenge in pharmaceutical research. Traditional analytical methods, such as cryo-electron microscopy, are labor-intensive, low-throughput, and often require labeling,...

A comparative analysis of deep learning architectures for thyroid tissue classification with hyperspectral imaging.

Scientific reports
Hyperspectral imaging has shown significant applicability in the medical field, particularly for its ability to represent spectral information that can differentiate specific biomolecular characteristics in tissue samples. However, the complexity of ...