AIMC Topic: Hyperspectral Imaging

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Exploring hyperspectral anomaly detection with human vision: A small target aware detector.

Neural networks : the official journal of the International Neural Network Society Dec 15, 2024
Hyperspectral anomaly detection (HAD) aims to localize pixel points whose spectral features differ from the background. HAD is essential in scenarios of unknown or camouflaged target features, such as water quality monitoring, crop growth monitoring ...
Humans Visual Perception Image Processing, Computer-Assisted Hyperspectral Imaging Neural Networks, Computer Algorithms
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Enhancing land cover object classification in hyperspectral imagery through an efficient spectral-spatial feature learning approach.

PloS one Dec 5, 2024
The classification of land cover objects in hyperspectral imagery (HSI) has significantly advanced due to the development of convolutional neural networks (CNNs). However, challenges such as limited training data and high dimensionality negatively im...
Hyperspectral Imaging Image Processing, Computer-Assisted Principal Component Analysis Neural Networks, Computer Algorithms
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Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques.

Sensors (Basel, Switzerland) Dec 4, 2024
Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human ...
Machine Learning Malus Image Processing, Computer-Assisted Plant Leaves Hyperspectral Imaging Plant Diseases Algorithms
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A green and efficient method for detecting nicosulfuron residues in field maize using hyperspectral imaging and deep learning.

Journal of hazardous materials Nov 30, 2024
Accurate and rapid detection of nicosulfuron herbicide residues in field-grown maize is essential for implementing chemical remediation and optimizing spraying strategies. However, current detection methods are costly and time-consuming. This study a...
Sulfonylurea Compounds Hyperspectral Imaging Pesticide Residues Zea mays Herbicides Deep Learning Plant Leaves Pyridines Neural Networks, Computer
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Evaluation and process monitoring of jujube hot air drying using hyperspectral imaging technology and deep learning for quality parameters.

Food chemistry Nov 12, 2024
Timely and effective detection of quality attributes during drying control is essential for enhancing the quality of fruit processing. Consequently, this study aims to employ hyperspectral imaging technology for the non-destructive monitoring of solu...
Hot Temperature Desiccation Hyperspectral Imaging Food Handling Deep Learning Fruit Ziziphus Quality Control
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Hyperspectral imaging analysis for early detection of tomato bacterial leaf spot disease.

Scientific reports Nov 12, 2024
Recent advancements in hyperspectral imaging (HSI) for early disease detection have shown promising results, yet there is a lack of validated high-resolution (spatial and spectral) HSI data representing the responses of plants at different stages of ...
Machine Learning Solanum lycopersicum Hyperspectral Imaging Plant Leaves Plant Diseases Xanthomonas
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Simultaneous monitoring of two comprehensive quality evaluation indexes of frozen-thawed beef meatballs using hyperspectral imaging and multi-task convolutional neural network.

Meat science Nov 10, 2024
The quality of beef meatballs during repeated freeze-thaw (F-T) cycles was assessed by multiple indicators. This study introduced a novel quality evaluation method using hyperspectral imaging (HSI) and multi-task learning. Seventeen quality indicator...
Red Meat Food Quality Cattle Meat Products Neural Networks, Computer Quality Control Hyperspectral Imaging Animals Freezing
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Hyperspectral imaging and deep learning for parasite detection in white fish under industrial conditions.

Scientific reports Nov 9, 2024
Parasites in fish muscle present a significant problem for the seafood industry in terms of both quality and health and safety, but the low contrast between parasites and fish tissue makes them exceedingly difficult to detect. The traditional method ...
Nematoda Food Parasitology Fish Diseases Fishes Seafood Hyperspectral Imaging Animals Deep Learning
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Milk adulteration identification using hyperspectral imaging and machine learning.

Journal of dairy science Nov 8, 2024
Milk adulteration poses a global concern, with developing countries facing higher risks due to unsatisfactory monitoring systems and policies. Surprisingly, this common issue has often been overlooked in many countries. Contrary to popular belief, ad...
Machine Learning Animals Milk Hyperspectral Imaging Cattle Food Contamination
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Dictionary trained attention constrained low rank and sparse autoencoder for hyperspectral anomaly detection.

Neural networks : the official journal of the International Neural Network Society Oct 11, 2024
Dictionary representations and deep learning Autoencoder (AE) models have proven effective in hyperspectral anomaly detection. Dictionary representations offer self-explanation but struggle with complex scenarios. Conversely, autoencoders can capture...
Neural Networks, Computer Image Processing, Computer-Assisted Attention Hyperspectral Imaging Algorithms Deep Learning
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