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Hyperspectral Imaging

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Hyperspectral imaging and deep learning for quantification of Clostridium sporogenes spores in food products using 1D- convolutional neural networks and random forest model.

Food research international (Ottawa, Ont.)
Clostridium sporogenes spores are used as surrogates for Clostridium botulinum, to verify thermal exposure and lethality in sterilization regimes by food industries. Conventional methods to detect spores are time-consuming and labour intensive. The o...

Machine learning classification of origins and varieties of Tetrastigma hemsleyanum using a dual-mode microscopic hyperspectral imager.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
A dual-mode microscopic hyperspectral imager (DMHI) combined with a machine learning algorithm for the purpose of classifying origins and varieties of Tetrastigma hemsleyanum (T. hemsleyanum) was developed. By switching the illumination source, the D...

Aflatoxin rapid detection based on hyperspectral with 1D-convolution neural network in the pixel level.

Food chemistry
Aflatoxin is commonly exists in moldy foods, it is classified as a class one carcinogen by the World Health Organization. In this paper, we used one dimensional convolution neural network (1D-CNN) to classify whether a pixel contains aflatoxin. First...

Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques.

Sensors (Basel, Switzerland)
Contamination is a critical issue that affects food consumption adversely. Therefore, efficient detection and classification of food contaminants are essential to ensure food safety. This study applied a visible and near-infrared (VNIR) hyperspectral...

Multimodal Deep Learning and Visible-Light and Hyperspectral Imaging for Fruit Maturity Estimation.

Sensors (Basel, Switzerland)
Fruit maturity is a critical factor in the supply chain, consumer preference, and agriculture industry. Most classification methods on fruit maturity identify only two classes: ripe and unripe, but this paper estimates six maturity stages of papaya f...

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning.

Sensors (Basel, Switzerland)
Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive...

Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging.

Scientific reports
The diagnosis of gastrointestinal stromal tumor (GIST) using conventional endoscopy is difficult because submucosal tumor (SMT) lesions like GIST are covered by a mucosal layer. Near-infrared hyperspectral imaging (NIR-HSI) can obtain optical informa...

Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms.

Sensors (Basel, Switzerland)
During the processing and planting of soybeans, it is greatly significant that a reliable, rapid, and accurate technique is used to detect soybean varieties. Traditional chemical analysis methods of soybean variety sampling (e.g., mass spectrometry a...

Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning.

Biosensors
Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence s...

Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks.

Sensors (Basel, Switzerland)
In recent years, growing interest in deep learning neural networks has raised a question on how they can be used for effective processing of high-dimensional datasets produced by hyperspectral imaging (HSI). HSI, traditionally viewed as being within ...