Nutritional Imaging in Agri-Food Systems

Jay Kumar Pandey, Tanmay Sarkar, Wing-Fu Lai

• Publisher: Wiley
• Year: 2026
• Print ISBN:
  9781394399611
• eBook ISBN:
  9781394399628
• Edition: 1
• Topic: Tecnología e ingeniería
• Subtopic: Ciencia de los alimentos

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:

• Detailed coverage of how cameras and sensors detect color, texture, and chemical composition to identify ripeness, spoilage, and fat content
• Practical applications for smartphones, drones, and smart farming equipment that make food analysis faster and more accessible across operations
• Integration strategies connecting machine learning, robotics, and remote sensing technologies for comprehensive agricultural and nutritional monitoring systems
• Methods for reducing food waste and improving food safety through automated quality control and real-time assessment at production scale
• Scalable solutions linking agriculture, nutrition, and digital innovation for healthier food systems and more efficient supply chain management

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.

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright Page
• Author Biographies
• About the Editors
• List of Contributors
• Preface
• Acknowledgments
• List of Abbreviations
• 1 Computational Foundations of Nutritional Imaging in Agri‐Food Systems
• 2 Image Acquisition Systems: RGB, Multispectral, and Hyperspectral Imaging Modalities
• 3 Preprocessing and Enhancement Techniques for Nutritional Feature Extraction
• 4 Segmentation and Object Detection in Crop and Food Images
• 5 Hyperspectral Image Analysis for Nondestructive Estimation of Crop Nutrient Composition
• 6 Computer Vision for Nutrition Deficiency in Field Crops
• 7 Soil Texture and Fertility Assessment Through Imaging
• 8 Vision‐Assisted Edge Framework for Real‐Time Food and Nutrition Analysis
• 9 Deep Learning Approaches for Nutritional Profiling of Food Plates
• 10 Consumer Preferences and Market Drivers for Varieties of Millet Products: A Study Among Urban and Semi‐Urban Consumers
• 11 Vision‐Based Quality Grading and Sorting in Food Processing Units
• 12 Real‐Time Nutritional Monitoring Using Edge AI and IoT Cameras
• 13 Computational Imaging Framework for Soil Texture Characterization and Nutrient Status Prediction
• 14 Consumer‐Centric Applications of Visual Nutrition Analysis
• 15 Applied Case Studies and Deployment Frameworks
• 16 Applications in Agriculture and Food Systems
• 17 Combining AI with Automated Quality Control in the Food Sector
• 18 Future Trends and Opportunities in Visualizing Nutrition
• Index
• End User License Agreement