Nutritional Imaging in Agri-Food Systems

1. Auflage 2026
ISBN: 978-1-394-39961-1
Verlag: John Wiley & Sons Inc

Buch, Englisch, 416 Seiten, Format (B × H): 176 mm x 253 mm, Gewicht: 936 g

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.

Autoren/Hrsg.

Pandey, Jay Kumar

Sarkar, Tanmay

Lai, Wing-Fu

Fachgebiete

• Naturwissenschaften Agrarwissenschaften Agrarwissenschaften Agrartechnik, Landmaschinen
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Lebensmitteltechnologie und Getränketechnologie
• Naturwissenschaften Chemie Chemie Allgemein Chemische Labormethoden, Stöchiometrie

Inhaltsverzeichnis

Author Biographies xix
About the Editors xxxiii
List of Contributors xxxv
Preface xxxix
Acknowledgments xli
List of Abbreviations xliii

1 Computational Foundations of Nutritional Imaging in Agri-Food Systems 1
Soumya Roy, Gouranga Bag, and Samrat Kundu

1.1 Introduction 1
1.2 Research Contributions 3
1.3 Literature Review 4
1.4 Methodology 7
1.5 Results 10
1.6 Discussion 11
1.7 Conclusion 13

2 Image Acquisition Systems: RGB, Multispectral, and Hyperspectral Imaging Modalities 17
Usman Muhammad Umar

2.1 Introduction 17
2.2 Types of Digital Imaging Modalities 19
2.3 Electromagnetic and Color Spectrum of Imaging Modalities 22
2.4 Emerging Technologies and Future Prospects 25
2.5 Conclusion 25

3 Preprocessing and Enhancement Techniques for Nutritional Feature Extraction 31
Sima Tahmouzi, Farhang Hameed Awlqadr, Javaneh Karimi, and Neda Mollakhalili Meybodi

3.1 Introduction 31
3.2 Image Quality Challenges and Correction Methods 32
3.3 Image Denoising and Enhancement 36
3.4 Spatial and Frequency Domain Noise Reduction 37
3.5 Contrast Enhancement and Histogram Equalization (Including CLAHE) 38
3.6 Color Normalization, Sharpening Filters, and Visibility Improvement for Segmentation 39
3.7 Radiometric and Geometric Corrections 40
3.8 Geometric Corrections (Lens Distortion, Sensor Geometry) and Image Registration Across Spectral Bands 42
3.9 Designing Automated Preprocessing Workflows for High-throughput Imaging 46
3.10 Conclusion and Future Directions 51

4 Segmentation and Object Detection in Crop and Food Images 59
Sagnik Bachhar, Sumanta Bhattacharjee, and Biswarup Yogi

4.1 Introduction 59
4.2 Fundamentals of Image Segmentation 61
4.3 Object Detection: Concepts and Techniques 64
4.4 Applications in Crop Imaging 66
4.5 Food Imaging Operations 68
4.6 Deep Learning and Model Architectures 71
4.7 Datasets and Tools 73
4.8 Challenges and Limitations 75
4.9 Future Directions 77
4.10 Conclusion 78

5 Hyperspectral Image Analysis for Nondestructive Estimation of Crop Nutrient Composition 81
Devanakonda Venkata Sai Chakradhar Reddy, Selvaprakash Ramalingam, and Divya Dharshini Saravanan

5.1 Introduction 81
5.2 Fundamentals of Hyperspectral Imaging 83
5.3 Nutrient Detection and Spectral Sensitivity 84
5.4 Data Acquisition Platforms 85
5.5 Preprocessing and Data Handling 87
5.6 Machine Learning and Analytical Techniques 87
5.7 Case Studies and Applications 88
5.8 Data Fusion and Integration 90
5.9 Challenges and Future Directions 92
5.10 Conclusion 92

6 Computer Vision for Nutrition Deficiency in Field Crops 99
Dilbar Hussain and Fahiza Fauz

6.1 Introduction 99
6.2 Chapter Aim and Scope 100
6.3 Types of Nutrient Deficiencies 102
6.4 Role of Computer Vision in Agriculture 105
6.5 Image Acquisition 107
6.6 Detection of Plant Stress, Deficiencies, and Diseases 108
6.7 Applications of Computer Vision in Agriculture 110
6.8 Disease and Pest Monitoring 110
6.9 Weed Detection 110
6.10 Yield Prediction 110
6.11 Monitoring Plant Growth 111
6.12 Recent Advancements in AI Applications for Agriculture 111
6.13 The Future of Computer Vision in Agriculture 111
6.14 Integration of Internet of Things Devices 112
6.15 Challenges with Traditional Detection Methods 114
6.16 Conclusion 114

7 Soil Texture and Fertility Assessment Through Imaging 119
Sanket Sunil Kawade, Panchakarla Sedyaaw, Huang Min, and Subrat Kumar Swain

7.1 Introduction 119
7.2 Soil Texture and Fertility: Fundamentals 120
7.3 Imaging Techniques for Soil Texture Analysis 121
7.4 Imaging Approaches for Soil Fertility Assessment 127
7.5 Integration of Imaging with Advanced Tools 128
7.6 Applications in Agriculture and Environmental Management 130
7.7 Current Trends and Future Prospects in Soil Texture and Fertility Assessment Through Imaging 131
7.8 Conclusion 131

8 Vision-Assisted Edge Framework for Real-Time Food and Nutrition Analysis 137
Hansa Rajput

8.1 Introduction 137
8.2 Background and Motivation 138
8.3 Core Architecture of Edge AI-based Nutritional Monitoring 140
8.4 Food Recognition and Nutritional Analysis 144
8.5 Enhancing Model Accuracy on Edge Devices 147
8.6 Multimodal Data Integration 149
8.7 Applications and Use Cases 151
8.8 Regulatory, Ethical, and Social Implications 154
8.9 Future Directions and Research Opportunities 156
8.10 Conclusion 157

9 Deep Learning Approaches for Nutritional Profiling of Food Plates 161
Hamadou Mamoudou

9.1 Introduction 161
9.2 Literature Review 162
9.3 Deep Learning Methods for Food Profiling 163
9.4 Datasets and Metrics 166
9.5 Applications and Impact 168
9.6 Challenges and Limits 169
9.7 Prospects 170
9.8 Conclusion 171

10 Consumer Preferences and Market Drivers for Varieties of Millet Products: A Study Among Urban and Semi-Urban Consumers 177
P. Sasikumar, T.M. Hemalatha, and V. Priyanka

10.1 Introduction 177
10.2 Research Problems 178
10.3 Objectives of the Study 178
10.4 Literature of the Study 179
10.5 Research Gap 179
10.6 Hypothesis 180
10.7 Research Methodology 181
10.8 Analysis and Interpretation 182
10.9 Regression Analysis 186
10.10 t-Test 188
10.11 One-way – ANOVA 190
10.12 Conclusion and Discussions 193
10.13 Recommendations 195

11 Vision-Based Quality Grading and Sorting in Food Processing Units 199
Hieu M. Tran, Tuan M. Le, Hung Son Nguyen, and Son V. T. Dao

11.1 Introduction 199
11.2 Related Works 200
11.3 Materials and Methods 202
11.4 Results and Discussions 210
11.5 Conclusion 214

12 Real-Time Nutritional Monitoring Using Edge AI and IoT Cameras 219
Mohammad Nasar, Mohammad Abu Kausar, and Md. Abu Nayyer

12.1 Introduction: Pioneering a Sustainable Agricultural Future 219
12.2 The Need for Real-Time Nutritional Monitoring 221
12.3 System Design: A Blueprint for Smart Farming 223
12.4 The AI Engine: Turning Images into Action 227
12.5 Real-World Impact: Case Studies and Applications 231
12.6 Building Trust: Explainable AI 233
12.7 Performance and Practicality 235
12.8 Conclusion and Future Horizons 236

13 Computational Imaging Framework for Soil Texture Characterization and Nutrient Status Prediction 241
Rohit Kumar Choudhury, Mouli Sarkar, Dipak Uttamrao Pakhre, Arijit Ghosh, Shubhadip Dasgupta, Mercy Chinneihoi Haokip, and Asim Biswas

13.1 Introduction 241
13.2 Spectral Imaging and Soil Sensing 243
13.3 Soil Emission Spectra, Physics of Soil Reflectance and Absorption Features 248
13.4 Image Acquisition Techniques 251
13.5 Image Processing 253
13.6 Computational Modeling and Statistics 258
13.7 Challenges and Limitations 263
13.8 Future Direction and Conclusion 263

14 Consumer-Centric Applications of Visual Nutrition Analysis 269
Shivani Phugat and Prabudh Goel

14.1 Introduction 269
14.2 The Science Behind Visual Nutrition Analysis 270
14.3 Technical Innovations and Implementation Challenges 275
14.4 Challenges and Future Directions 277
14.5 Future of Visual Nutrition Analysis 279
14.6 Conclusion 280

15 Applied Case Studies and Deployment Frameworks 287
Selvaprakash Ramalingam and Devanakonda Venkata Sai Chakradhar Reddy

15.1 Introduction to Edge-AI Deployment Framework 287
15.2 Hardware and Edge Node Architecture 288
15.3 Sensor Fusion and Preprocessing Techniques 290
15.4 Advanced Computer Vision and Neural Inference Pipelines 290
15.5 Postprocessing Analytics and Spatiotemporal Filtering 291
15.6 Data Transmission, Federation, and Cloud Integration 292
15.7 Autonomous Variable-Rate Application and Feedback Control 292
15.8 System Reliability, Security, and Over-the-Air Management 292
15.9 Field Validation and Performance Metrics 294
15.10 Scalability, Modularity, and Future Directions 295
15.11 Conclusions 296
15.12 Key Definitions 297

16 Applications in Agriculture and Food Systems 301
N. Saheedha

16.1 Introduction 301
16.2 Image Processing in Food Systems 308
16.3 Food Quality and Safety Assessment 308
16.4 Integration with Machine Learning (ML) and Artificial Intelligence (AI) 312
16.5 Challenges and Limitations 314
16.6 Future Perspectives and Emerging Trends 315
16.7 Conclusion 315

17 Combining AI with Automated Quality Control in the Food Sector 321
B. Leena, S. Karthikeyan, and Razaidi Hussin

17.1 Introduction 321
17.2 Literature Survey 324
17.3 System Design 326
17.4 Result and Discussion 329
17.5 Conclusion 333

18 Future Trends and Opportunities in Visualizing Nutrition 337
Pushkar Singh Rawat and Shalini Singh

18.1 Introduction 337
18.2 Technological Advancements Shaping Nutrition Visualization 339
18.3 Emerging Visualization Modalities 340
18.4 Personalized and Precision Nutrition Visualization 342
18.5 Applications Across Health, Policy, and Industry 344
18.6 Challenges and Ethical Considerations 345
18.7 Future Directions and Research Opportunities 347
18.8 Conclusion 349

References 350
Index 355

Über Autor(innen)

JAY KUMAR PANDEY is an Assistant Professor in the Department of Electrical & Electronics Engineering at Shri Ramswaroop Memorial University, India. With fifteen years of teaching and research experience, he has published more than thirty research papers and books.

TANMAY SARKAR is a Lecturer in Food Processing Technology at West Bengal State Council of Technical Education, Government of West Bengal. He has authored more than 140 research papers including 90 SCIE/SCI publications. Dr. Sarkar has been classified among the top 2% of global highly-cited researchers published by Stanford University, USA in the year 2024 and 2025.

WING-FU LAI is an Associate Professor at the School of Food Science and Nutrition, University of Leeds, UK. He is also an Honorary Professor at Zhejiang Provincial People’s Hospital. His research focuses on developing and engineering functional polymeric materials for bioactive agent encapsulation and food product innovation.