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Rapid non-destructive nutritional analysis using advanced imaging technologies
Traditional nutritional analysis methods are destructive, slow, and impractical for large-scale applications. Nutritional Imaging in Agri-Food Systems presents image processing as a rapid, non-destructive alternative with real-time monitoring capabilities. A team of specialists in food science, imaging, and data analytics demonstrates how visual data can assess quality, detect adulteration, and monitor safety with unprecedented speed.
This volume covers imaging technologies from visible light to multispectral, hyperspectral, thermal, fluorescence, and microscopy techniques, detailing specific applications in nutritional assessment. Case studies demonstrate scalable utilization in agriculture and food systems, including spoilage detection, automated grading, and real-time monitoring using edge AI and IoT. The book integrates machine learning, remote sensing, and digital agriculture insights.
Readers will also find:
Written for scientists in agricultural science, food science, nutrition, computer vision, and image processing, this reference serves professionals in food processing, quality control, and agricultural technology. Regulatory agencies and remote sensing specialists will find practical frameworks for leveraging technology in food monitoring and policy development.
Rapid non-destructive nutritional analysis using advanced imaging technologies
Traditional nutritional analysis methods are destructive, slow, and impractical for large-scale applications. Nutritional Imaging in Agri-Food Systems presents image processing as a rapid, non-destructive alternative with real-time monitoring capabilities. A team of specialists in food science, imaging, and data analytics demonstrates how visual data can assess quality, detect adulteration, and monitor safety with unprecedented speed.
This volume covers imaging technologies from visible light to multispectral, hyperspectral, thermal, fluorescence, and microscopy techniques, detailing specific applications in nutritional assessment. Case studies demonstrate scalable utilization in agriculture and food systems, including spoilage detection, automated grading, and real-time monitoring using edge AI and IoT. The book integrates machine learning, remote sensing, and digital agriculture insights.
Readers will also find:
Written for scientists in agricultural science, food science, nutrition, computer vision, and image processing, this reference serves professionals in food processing, quality control, and agricultural technology. Regulatory agencies and remote sensing specialists will find practical frameworks for leveraging technology in food monitoring and policy development.
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