AIMC Topic: Color

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Low-cost machine learning-integrated optical spectrophotometer for non-destructive color and shelf-life analysis: A study on sliced bread.

Food chemistry
Monitoring physical color and spectral signatures is essential for the early detection of spoilage in perishable food products. This study introduces a cost-effective, Machine Learning (ML)-enabled spectrophotometer for nondestructive spoilage detect...

Artificial intelligence-driven food quality prediction: Applying machine learning ensemble models for dynamic forecasting of pork pH and meat color changes.

Food chemistry
This study presents a food chemistry-driven approach to predict post-slaughter pork quality dynamics, focusing on the biochemical mechanisms governing pH evolution and meat color development over 48 h. The interconversion of myoglobin redox states an...

Automatic cattle identification system based on color point cloud using hybrid PointNet++ Siamese network.

Scientific reports
Cattle health monitoring and management systems are essential for farmers and veterinarians, as traditional manual health checks can be time-consuming and labor-intensive. A critical aspect of such systems is accurate cattle identification, which ena...

Machine reading and recovery of colors for hemoglobin-related bioassays and bioimaging.

Science advances
Despite advances in machine learning and computer vision for biomedical imaging, machine reading and learning of colors remain underexplored. Color consistency in computer vision, color constancy in human perception, and color accuracy in biomedical ...

Digital image processing combined with machine learning: A novel approach for bee pollen classification.

Food research international (Ottawa, Ont.)
The classification of bee pollen is crucial for ensuring product authenticity, quality control, and fraud prevention, particularly given the high commercial value of stingless bee pot-pollen. Although traditional pollen analysis methods are available...

Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling.

Sensors (Basel, Switzerland)
Chlorophyll content in date leaves is critical for fruit quality and yield. Traditional detection methods are usually complex and expensive. This study proposes a rapid detection method for chlorophyll content using smartphone images and machine lear...

Detection of ninhydrin-glyphosate in groundwater via the colour chart-assisted digital camera method.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study introduces a novel application of a one-time and multistandard colour chart across three instruments using colour visible spectrophotometer (colour VS), digital single-lens reflex (DSLR) cameras, and mobile camera (MC) analysis to quantify...

Computer Vision in Monitoring Fruit Browning: Neural Networks vs. Stochastic Modelling.

Sensors (Basel, Switzerland)
As human labour is limited and therefore expensive, computer vision has emerged as a solution with encouraging results for monitoring and sorting tasks in the agrifood sector, where conventional methods for inspecting fruit browning that are generall...

Delayed flowering phenology of red-flowering plants in response to hummingbird migration.

Current biology : CB
The radiation of angiosperms is marked by a phenomenal diversity of floral size, shape, color, scent, and reward. The multi-dimensional response to selection to optimize pollination has generated correlated suites of these floral traits across distan...

Prior Visual-Guided Self-Supervised Learning Enables Color Vignetting Correction for High-Throughput Microscopic Imaging.

IEEE journal of biomedical and health informatics
Vignetting constitutes a prevalent optical degradation that significantly compromises the quality of biomedical microscopic imaging. However, a robust and efficient vignetting correction methodology in multi-channel microscopic images remains absent ...