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.

• Author Biographies xixAbout the Editors xxxiiiList of Contributors xxxvPreface xxxixAcknowledgments xliList of Abbreviations xliii1 Computational Foundations of Nutritional Imaging in Agri-Food Systems 1Soumya Roy, Gouranga Bag, and Samrat Kundu1.1 Introduction 11.2 Research Contributions 31.3 Literature Review 41.4 Methodology 71.5 Results 101.6 Discussion 111.7 Conclusion 132 Image Acquisition Systems: RGB, Multispectral, and Hyperspectral Imaging Modalities 17Usman Muhammad Umar2.1 Introduction 172.2 Types of Digital Imaging Modalities 192.3 Electromagnetic and Color Spectrum of Imaging Modalities 222.4 Emerging Technologies and Future Prospects 252.5 Conclusion 253 Preprocessing and Enhancement Techniques for Nutritional Feature Extraction 31Sima Tahmouzi, Farhang Hameed Awlqadr, Javaneh Karimi, and Neda Mollakhalili Meybodi3.1 Introduction 313.2 Image Quality Challenges and Correction Methods 323.3 Image Denoising and Enhancement 363.4 Spatial and Frequency Domain Noise Reduction 373.5 Contrast Enhancement and Histogram Equalization (Including CLAHE) 383.6 Color Normalization, Sharpening Filters, and Visibility Improvement for Segmentation 393.7 Radiometric and Geometric Corrections 403.8 Geometric Corrections (Lens Distortion, Sensor Geometry) and Image Registration Across Spectral Bands 423.9 Designing Automated Preprocessing Workflows for High-throughput Imaging 463.10 Conclusion and Future Directions 514 Segmentation and Object Detection in Crop and Food Images 59Sagnik Bachhar, Sumanta Bhattacharjee, and Biswarup Yogi4.1 Introduction 594.2 Fundamentals of Image Segmentation 614.3 Object Detection: Concepts and Techniques 644.4 Applications in Crop Imaging 664.5 Food Imaging Operations 684.6 Deep Learning and Model Architectures 714.7 Datasets and Tools 734.8 Challenges and Limitations 754.9 Future Directions 774.10 Conclusion 785 Hyperspectral Image Analysis for Nondestructive Estimation of Crop Nutrient Composition 81Devanakonda Venkata Sai Chakradhar Reddy, Selvaprakash Ramalingam, and Divya Dharshini Saravanan5.1 Introduction 815.2 Fundamentals of Hyperspectral Imaging 835.3 Nutrient Detection and Spectral Sensitivity 845.4 Data Acquisition Platforms 855.5 Preprocessing and Data Handling 875.6 Machine Learning and Analytical Techniques 875.7 Case Studies and Applications 885.8 Data Fusion and Integration 905.9 Challenges and Future Directions 925.10 Conclusion 926 Computer Vision for Nutrition Deficiency in Field Crops 99Dilbar Hussain and Fahiza Fauz6.1 Introduction 996.2 Chapter Aim and Scope 1006.3 Types of Nutrient Deficiencies 1026.4 Role of Computer Vision in Agriculture 1056.5 Image Acquisition 1076.6 Detection of Plant Stress, Deficiencies, and Diseases 1086.7 Applications of Computer Vision in Agriculture 1106.8 Disease and Pest Monitoring 1106.9 Weed Detection 1106.10 Yield Prediction 1106.11 Monitoring Plant Growth 1116.12 Recent Advancements in AI Applications for Agriculture 1116.13 The Future of Computer Vision in Agriculture 1116.14 Integration of Internet of Things Devices 1126.15 Challenges with Traditional Detection Methods 1146.16 Conclusion 1147 Soil Texture and Fertility Assessment Through Imaging 119Sanket Sunil Kawade, Panchakarla Sedyaaw, Huang Min, and Subrat Kumar Swain7.1 Introduction 1197.2 Soil Texture and Fertility: Fundamentals 1207.3 Imaging Techniques for Soil Texture Analysis 1217.4 Imaging Approaches for Soil Fertility Assessment 1277.5 Integration of Imaging with Advanced Tools 1287.6 Applications in Agriculture and Environmental Management 1307.7 Current Trends and Future Prospects in Soil Texture and Fertility Assessment Through Imaging 1317.8 Conclusion 1318 Vision-Assisted Edge Framework for Real-Time Food and Nutrition Analysis 137Hansa Rajput8.1 Introduction 1378.2 Background and Motivation 1388.3 Core Architecture of Edge AI-based Nutritional Monitoring 1408.4 Food Recognition and Nutritional Analysis 1448.5 Enhancing Model Accuracy on Edge Devices 1478.6 Multimodal Data Integration 1498.7 Applications and Use Cases 1518.8 Regulatory, Ethical, and Social Implications 1548.9 Future Directions and Research Opportunities 1568.10 Conclusion 1579 Deep Learning Approaches for Nutritional Profiling of Food Plates 161Hamadou Mamoudou9.1 Introduction 1619.2 Literature Review 1629.3 Deep Learning Methods for Food Profiling 1639.4 Datasets and Metrics 1669.5 Applications and Impact 1689.6 Challenges and Limits 1699.7 Prospects 1709.8 Conclusion 17110 Consumer Preferences and Market Drivers for Varieties of Millet Products: A Study Among Urban and Semi-Urban Consumers 177P. Sasikumar, T.M. Hemalatha, and V. Priyanka10.1 Introduction 17710.2 Research Problems 17810.3 Objectives of the Study 17810.4 Literature of the Study 17910.5 Research Gap 17910.6 Hypothesis 18010.7 Research Methodology 18110.8 Analysis and Interpretation 18210.9 Regression Analysis 18610.10 t-Test 18810.11 One-way – ANOVA 19010.12 Conclusion and Discussions 19310.13 Recommendations 19511 Vision-Based Quality Grading and Sorting in Food Processing Units 199Hieu M. Tran, Tuan M. Le, Hung Son Nguyen, and Son V. T. Dao11.1 Introduction 19911.2 Related Works 20011.3 Materials and Methods 20211.4 Results and Discussions 21011.5 Conclusion 21412 Real-Time Nutritional Monitoring Using Edge AI and IoT Cameras 219Mohammad Nasar, Mohammad Abu Kausar, and Md. Abu Nayyer12.1 Introduction: Pioneering a Sustainable Agricultural Future 21912.2 The Need for Real-Time Nutritional Monitoring 22112.3 System Design: A Blueprint for Smart Farming 22312.4 The AI Engine: Turning Images into Action 22712.5 Real-World Impact: Case Studies and Applications 23112.6 Building Trust: Explainable AI 23312.7 Performance and Practicality 23512.8 Conclusion and Future Horizons 23613 Computational Imaging Framework for Soil Texture Characterization and Nutrient Status Prediction 241Rohit Kumar Choudhury, Mouli Sarkar, Dipak Uttamrao Pakhre, Arijit Ghosh, Shubhadip Dasgupta, Mercy Chinneihoi Haokip, and Asim Biswas13.1 Introduction 24113.2 Spectral Imaging and Soil Sensing 24313.3 Soil Emission Spectra, Physics of Soil Reflectance and Absorption Features 24813.4 Image Acquisition Techniques 25113.5 Image Processing 25313.6 Computational Modeling and Statistics 25813.7 Challenges and Limitations 26313.8 Future Direction and Conclusion 26314 Consumer-Centric Applications of Visual Nutrition Analysis 269Shivani Phugat and Prabudh Goel14.1 Introduction 26914.2 The Science Behind Visual Nutrition Analysis 27014.3 Technical Innovations and Implementation Challenges 27514.4 Challenges and Future Directions 27714.5 Future of Visual Nutrition Analysis 27914.6 Conclusion 28015 Applied Case Studies and Deployment Frameworks 287Selvaprakash Ramalingam and Devanakonda Venkata Sai Chakradhar Reddy15.1 Introduction to Edge-AI Deployment Framework 28715.2 Hardware and Edge Node Architecture 28815.3 Sensor Fusion and Preprocessing Techniques 29015.4 Advanced Computer Vision and Neural Inference Pipelines 29015.5 Postprocessing Analytics and Spatiotemporal Filtering 29115.6 Data Transmission, Federation, and Cloud Integration 29215.7 Autonomous Variable-Rate Application and Feedback Control 29215.8 System Reliability, Security, and Over-the-Air Management 29215.9 Field Validation and Performance Metrics 29415.10 Scalability, Modularity, and Future Directions 29515.11 Conclusions 29615.12 Key Definitions 29716 Applications in Agriculture and Food Systems 301N. Saheedha16.1 Introduction 30116.2 Image Processing in Food Systems 30816.3 Food Quality and Safety Assessment 30816.4 Integration with Machine Learning (ML) and Artificial Intelligence (AI) 31216.5 Challenges and Limitations 31416.6 Future Perspectives and Emerging Trends 31516.7 Conclusion 31517 Combining AI with Automated Quality Control in the Food Sector 321B. Leena, S. Karthikeyan, and Razaidi Hussin17.1 Introduction 32117.2 Literature Survey 32417.3 System Design 32617.4 Result and Discussion 32917.5 Conclusion 33318 Future Trends and Opportunities in Visualizing Nutrition 337Pushkar Singh Rawat and Shalini Singh18.1 Introduction 33718.2 Technological Advancements Shaping Nutrition Visualization 33918.3 Emerging Visualization Modalities 34018.4 Personalized and Precision Nutrition Visualization 34218.5 Applications Across Health, Policy, and Industry 34418.6 Challenges and Ethical Considerations 34518.7 Future Directions and Research Opportunities 34718.8 Conclusion 349References 350Index 355

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.

• Author Biographies xixAbout the Editors xxxiiiList of Contributors xxxvPreface xxxixAcknowledgments xliList of Abbreviations xliii1 Computational Foundations of Nutritional Imaging in Agri-Food Systems 1Soumya Roy, Gouranga Bag, and Samrat Kundu1.1 Introduction 11.2 Research Contributions 31.3 Literature Review 41.4 Methodology 71.5 Results 101.6 Discussion 111.7 Conclusion 132 Image Acquisition Systems: RGB, Multispectral, and Hyperspectral Imaging Modalities 17Usman Muhammad Umar2.1 Introduction 172.2 Types of Digital Imaging Modalities 192.3 Electromagnetic and Color Spectrum of Imaging Modalities 222.4 Emerging Technologies and Future Prospects 252.5 Conclusion 253 Preprocessing and Enhancement Techniques for Nutritional Feature Extraction 31Sima Tahmouzi, Farhang Hameed Awlqadr, Javaneh Karimi, and Neda Mollakhalili Meybodi3.1 Introduction 313.2 Image Quality Challenges and Correction Methods 323.3 Image Denoising and Enhancement 363.4 Spatial and Frequency Domain Noise Reduction 373.5 Contrast Enhancement and Histogram Equalization (Including CLAHE) 383.6 Color Normalization, Sharpening Filters, and Visibility Improvement for Segmentation 393.7 Radiometric and Geometric Corrections 403.8 Geometric Corrections (Lens Distortion, Sensor Geometry) and Image Registration Across Spectral Bands 423.9 Designing Automated Preprocessing Workflows for High-throughput Imaging 463.10 Conclusion and Future Directions 514 Segmentation and Object Detection in Crop and Food Images 59Sagnik Bachhar, Sumanta Bhattacharjee, and Biswarup Yogi4.1 Introduction 594.2 Fundamentals of Image Segmentation 614.3 Object Detection: Concepts and Techniques 644.4 Applications in Crop Imaging 664.5 Food Imaging Operations 684.6 Deep Learning and Model Architectures 714.7 Datasets and Tools 734.8 Challenges and Limitations 754.9 Future Directions 774.10 Conclusion 785 Hyperspectral Image Analysis for Nondestructive Estimation of Crop Nutrient Composition 81Devanakonda Venkata Sai Chakradhar Reddy, Selvaprakash Ramalingam, and Divya Dharshini Saravanan5.1 Introduction 815.2 Fundamentals of Hyperspectral Imaging 835.3 Nutrient Detection and Spectral Sensitivity 845.4 Data Acquisition Platforms 855.5 Preprocessing and Data Handling 875.6 Machine Learning and Analytical Techniques 875.7 Case Studies and Applications 885.8 Data Fusion and Integration 905.9 Challenges and Future Directions 925.10 Conclusion 926 Computer Vision for Nutrition Deficiency in Field Crops 99Dilbar Hussain and Fahiza Fauz6.1 Introduction 996.2 Chapter Aim and Scope 1006.3 Types of Nutrient Deficiencies 1026.4 Role of Computer Vision in Agriculture 1056.5 Image Acquisition 1076.6 Detection of Plant Stress, Deficiencies, and Diseases 1086.7 Applications of Computer Vision in Agriculture 1106.8 Disease and Pest Monitoring 1106.9 Weed Detection 1106.10 Yield Prediction 1106.11 Monitoring Plant Growth 1116.12 Recent Advancements in AI Applications for Agriculture 1116.13 The Future of Computer Vision in Agriculture 1116.14 Integration of Internet of Things Devices 1126.15 Challenges with Traditional Detection Methods 1146.16 Conclusion 1147 Soil Texture and Fertility Assessment Through Imaging 119Sanket Sunil Kawade, Panchakarla Sedyaaw, Huang Min, and Subrat Kumar Swain7.1 Introduction 1197.2 Soil Texture and Fertility: Fundamentals 1207.3 Imaging Techniques for Soil Texture Analysis 1217.4 Imaging Approaches for Soil Fertility Assessment 1277.5 Integration of Imaging with Advanced Tools 1287.6 Applications in Agriculture and Environmental Management 1307.7 Current Trends and Future Prospects in Soil Texture and Fertility Assessment Through Imaging 1317.8 Conclusion 1318 Vision-Assisted Edge Framework for Real-Time Food and Nutrition Analysis 137Hansa Rajput8.1 Introduction 1378.2 Background and Motivation 1388.3 Core Architecture of Edge AI-based Nutritional Monitoring 1408.4 Food Recognition and Nutritional Analysis 1448.5 Enhancing Model Accuracy on Edge Devices 1478.6 Multimodal Data Integration 1498.7 Applications and Use Cases 1518.8 Regulatory, Ethical, and Social Implications 1548.9 Future Directions and Research Opportunities 1568.10 Conclusion 1579 Deep Learning Approaches for Nutritional Profiling of Food Plates 161Hamadou Mamoudou9.1 Introduction 1619.2 Literature Review 1629.3 Deep Learning Methods for Food Profiling 1639.4 Datasets and Metrics 1669.5 Applications and Impact 1689.6 Challenges and Limits 1699.7 Prospects 1709.8 Conclusion 17110 Consumer Preferences and Market Drivers for Varieties of Millet Products: A Study Among Urban and Semi-Urban Consumers 177P. Sasikumar, T.M. Hemalatha, and V. Priyanka10.1 Introduction 17710.2 Research Problems 17810.3 Objectives of the Study 17810.4 Literature of the Study 17910.5 Research Gap 17910.6 Hypothesis 18010.7 Research Methodology 18110.8 Analysis and Interpretation 18210.9 Regression Analysis 18610.10 t-Test 18810.11 One-way – ANOVA 19010.12 Conclusion and Discussions 19310.13 Recommendations 19511 Vision-Based Quality Grading and Sorting in Food Processing Units 199Hieu M. Tran, Tuan M. Le, Hung Son Nguyen, and Son V. T. Dao11.1 Introduction 19911.2 Related Works 20011.3 Materials and Methods 20211.4 Results and Discussions 21011.5 Conclusion 21412 Real-Time Nutritional Monitoring Using Edge AI and IoT Cameras 219Mohammad Nasar, Mohammad Abu Kausar, and Md. Abu Nayyer12.1 Introduction: Pioneering a Sustainable Agricultural Future 21912.2 The Need for Real-Time Nutritional Monitoring 22112.3 System Design: A Blueprint for Smart Farming 22312.4 The AI Engine: Turning Images into Action 22712.5 Real-World Impact: Case Studies and Applications 23112.6 Building Trust: Explainable AI 23312.7 Performance and Practicality 23512.8 Conclusion and Future Horizons 23613 Computational Imaging Framework for Soil Texture Characterization and Nutrient Status Prediction 241Rohit Kumar Choudhury, Mouli Sarkar, Dipak Uttamrao Pakhre, Arijit Ghosh, Shubhadip Dasgupta, Mercy Chinneihoi Haokip, and Asim Biswas13.1 Introduction 24113.2 Spectral Imaging and Soil Sensing 24313.3 Soil Emission Spectra, Physics of Soil Reflectance and Absorption Features 24813.4 Image Acquisition Techniques 25113.5 Image Processing 25313.6 Computational Modeling and Statistics 25813.7 Challenges and Limitations 26313.8 Future Direction and Conclusion 26314 Consumer-Centric Applications of Visual Nutrition Analysis 269Shivani Phugat and Prabudh Goel14.1 Introduction 26914.2 The Science Behind Visual Nutrition Analysis 27014.3 Technical Innovations and Implementation Challenges 27514.4 Challenges and Future Directions 27714.5 Future of Visual Nutrition Analysis 27914.6 Conclusion 28015 Applied Case Studies and Deployment Frameworks 287Selvaprakash Ramalingam and Devanakonda Venkata Sai Chakradhar Reddy15.1 Introduction to Edge-AI Deployment Framework 28715.2 Hardware and Edge Node Architecture 28815.3 Sensor Fusion and Preprocessing Techniques 29015.4 Advanced Computer Vision and Neural Inference Pipelines 29015.5 Postprocessing Analytics and Spatiotemporal Filtering 29115.6 Data Transmission, Federation, and Cloud Integration 29215.7 Autonomous Variable-Rate Application and Feedback Control 29215.8 System Reliability, Security, and Over-the-Air Management 29215.9 Field Validation and Performance Metrics 29415.10 Scalability, Modularity, and Future Directions 29515.11 Conclusions 29615.12 Key Definitions 29716 Applications in Agriculture and Food Systems 301N. Saheedha16.1 Introduction 30116.2 Image Processing in Food Systems 30816.3 Food Quality and Safety Assessment 30816.4 Integration with Machine Learning (ML) and Artificial Intelligence (AI) 31216.5 Challenges and Limitations 31416.6 Future Perspectives and Emerging Trends 31516.7 Conclusion 31517 Combining AI with Automated Quality Control in the Food Sector 321B. Leena, S. Karthikeyan, and Razaidi Hussin17.1 Introduction 32117.2 Literature Survey 32417.3 System Design 32617.4 Result and Discussion 32917.5 Conclusion 33318 Future Trends and Opportunities in Visualizing Nutrition 337Pushkar Singh Rawat and Shalini Singh18.1 Introduction 33718.2 Technological Advancements Shaping Nutrition Visualization 33918.3 Emerging Visualization Modalities 34018.4 Personalized and Precision Nutrition Visualization 34218.5 Applications Across Health, Policy, and Industry 34418.6 Challenges and Ethical Considerations 34518.7 Future Directions and Research Opportunities 34718.8 Conclusion 349References 350Index 355

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.

• Author Biographies xixAbout the Editors xxxiiiList of Contributors xxxvPreface xxxixAcknowledgments xliList of Abbreviations xliii1 Computational Foundations of Nutritional Imaging in Agri-Food Systems 1Soumya Roy, Gouranga Bag, and Samrat Kundu1.1 Introduction 11.2 Research Contributions 31.3 Literature Review 41.4 Methodology 71.5 Results 101.6 Discussion 111.7 Conclusion 132 Image Acquisition Systems: RGB, Multispectral, and Hyperspectral Imaging Modalities 17Usman Muhammad Umar2.1 Introduction 172.2 Types of Digital Imaging Modalities 192.3 Electromagnetic and Color Spectrum of Imaging Modalities 222.4 Emerging Technologies and Future Prospects 252.5 Conclusion 253 Preprocessing and Enhancement Techniques for Nutritional Feature Extraction 31Sima Tahmouzi, Farhang Hameed Awlqadr, Javaneh Karimi, and Neda Mollakhalili Meybodi3.1 Introduction 313.2 Image Quality Challenges and Correction Methods 323.3 Image Denoising and Enhancement 363.4 Spatial and Frequency Domain Noise Reduction 373.5 Contrast Enhancement and Histogram Equalization (Including CLAHE) 383.6 Color Normalization, Sharpening Filters, and Visibility Improvement for Segmentation 393.7 Radiometric and Geometric Corrections 403.8 Geometric Corrections (Lens Distortion, Sensor Geometry) and Image Registration Across Spectral Bands 423.9 Designing Automated Preprocessing Workflows for High-throughput Imaging 463.10 Conclusion and Future Directions 514 Segmentation and Object Detection in Crop and Food Images 59Sagnik Bachhar, Sumanta Bhattacharjee, and Biswarup Yogi4.1 Introduction 594.2 Fundamentals of Image Segmentation 614.3 Object Detection: Concepts and Techniques 644.4 Applications in Crop Imaging 664.5 Food Imaging Operations 684.6 Deep Learning and Model Architectures 714.7 Datasets and Tools 734.8 Challenges and Limitations 754.9 Future Directions 774.10 Conclusion 785 Hyperspectral Image Analysis for Nondestructive Estimation of Crop Nutrient Composition 81Devanakonda Venkata Sai Chakradhar Reddy, Selvaprakash Ramalingam, and Divya Dharshini Saravanan5.1 Introduction 815.2 Fundamentals of Hyperspectral Imaging 835.3 Nutrient Detection and Spectral Sensitivity 845.4 Data Acquisition Platforms 855.5 Preprocessing and Data Handling 875.6 Machine Learning and Analytical Techniques 875.7 Case Studies and Applications 885.8 Data Fusion and Integration 905.9 Challenges and Future Directions 925.10 Conclusion 926 Computer Vision for Nutrition Deficiency in Field Crops 99Dilbar Hussain and Fahiza Fauz6.1 Introduction 996.2 Chapter Aim and Scope 1006.3 Types of Nutrient Deficiencies 1026.4 Role of Computer Vision in Agriculture 1056.5 Image Acquisition 1076.6 Detection of Plant Stress, Deficiencies, and Diseases 1086.7 Applications of Computer Vision in Agriculture 1106.8 Disease and Pest Monitoring 1106.9 Weed Detection 1106.10 Yield Prediction 1106.11 Monitoring Plant Growth 1116.12 Recent Advancements in AI Applications for Agriculture 1116.13 The Future of Computer Vision in Agriculture 1116.14 Integration of Internet of Things Devices 1126.15 Challenges with Traditional Detection Methods 1146.16 Conclusion 1147 Soil Texture and Fertility Assessment Through Imaging 119Sanket Sunil Kawade, Panchakarla Sedyaaw, Huang Min, and Subrat Kumar Swain7.1 Introduction 1197.2 Soil Texture and Fertility: Fundamentals 1207.3 Imaging Techniques for Soil Texture Analysis 1217.4 Imaging Approaches for Soil Fertility Assessment 1277.5 Integration of Imaging with Advanced Tools 1287.6 Applications in Agriculture and Environmental Management 1307.7 Current Trends and Future Prospects in Soil Texture and Fertility Assessment Through Imaging 1317.8 Conclusion 1318 Vision-Assisted Edge Framework for Real-Time Food and Nutrition Analysis 137Hansa Rajput8.1 Introduction 1378.2 Background and Motivation 1388.3 Core Architecture of Edge AI-based Nutritional Monitoring 1408.4 Food Recognition and Nutritional Analysis 1448.5 Enhancing Model Accuracy on Edge Devices 1478.6 Multimodal Data Integration 1498.7 Applications and Use Cases 1518.8 Regulatory, Ethical, and Social Implications 1548.9 Future Directions and Research Opportunities 1568.10 Conclusion 1579 Deep Learning Approaches for Nutritional Profiling of Food Plates 161Hamadou Mamoudou9.1 Introduction 1619.2 Literature Review 1629.3 Deep Learning Methods for Food Profiling 1639.4 Datasets and Metrics 1669.5 Applications and Impact 1689.6 Challenges and Limits 1699.7 Prospects 1709.8 Conclusion 17110 Consumer Preferences and Market Drivers for Varieties of Millet Products: A Study Among Urban and Semi-Urban Consumers 177P. Sasikumar, T.M. Hemalatha, and V. Priyanka10.1 Introduction 17710.2 Research Problems 17810.3 Objectives of the Study 17810.4 Literature of the Study 17910.5 Research Gap 17910.6 Hypothesis 18010.7 Research Methodology 18110.8 Analysis and Interpretation 18210.9 Regression Analysis 18610.10 t-Test 18810.11 One-way – ANOVA 19010.12 Conclusion and Discussions 19310.13 Recommendations 19511 Vision-Based Quality Grading and Sorting in Food Processing Units 199Hieu M. Tran, Tuan M. Le, Hung Son Nguyen, and Son V. T. Dao11.1 Introduction 19911.2 Related Works 20011.3 Materials and Methods 20211.4 Results and Discussions 21011.5 Conclusion 21412 Real-Time Nutritional Monitoring Using Edge AI and IoT Cameras 219Mohammad Nasar, Mohammad Abu Kausar, and Md. Abu Nayyer12.1 Introduction: Pioneering a Sustainable Agricultural Future 21912.2 The Need for Real-Time Nutritional Monitoring 22112.3 System Design: A Blueprint for Smart Farming 22312.4 The AI Engine: Turning Images into Action 22712.5 Real-World Impact: Case Studies and Applications 23112.6 Building Trust: Explainable AI 23312.7 Performance and Practicality 23512.8 Conclusion and Future Horizons 23613 Computational Imaging Framework for Soil Texture Characterization and Nutrient Status Prediction 241Rohit Kumar Choudhury, Mouli Sarkar, Dipak Uttamrao Pakhre, Arijit Ghosh, Shubhadip Dasgupta, Mercy Chinneihoi Haokip, and Asim Biswas13.1 Introduction 24113.2 Spectral Imaging and Soil Sensing 24313.3 Soil Emission Spectra, Physics of Soil Reflectance and Absorption Features 24813.4 Image Acquisition Techniques 25113.5 Image Processing 25313.6 Computational Modeling and Statistics 25813.7 Challenges and Limitations 26313.8 Future Direction and Conclusion 26314 Consumer-Centric Applications of Visual Nutrition Analysis 269Shivani Phugat and Prabudh Goel14.1 Introduction 26914.2 The Science Behind Visual Nutrition Analysis 27014.3 Technical Innovations and Implementation Challenges 27514.4 Challenges and Future Directions 27714.5 Future of Visual Nutrition Analysis 27914.6 Conclusion 28015 Applied Case Studies and Deployment Frameworks 287Selvaprakash Ramalingam and Devanakonda Venkata Sai Chakradhar Reddy15.1 Introduction to Edge-AI Deployment Framework 28715.2 Hardware and Edge Node Architecture 28815.3 Sensor Fusion and Preprocessing Techniques 29015.4 Advanced Computer Vision and Neural Inference Pipelines 29015.5 Postprocessing Analytics and Spatiotemporal Filtering 29115.6 Data Transmission, Federation, and Cloud Integration 29215.7 Autonomous Variable-Rate Application and Feedback Control 29215.8 System Reliability, Security, and Over-the-Air Management 29215.9 Field Validation and Performance Metrics 29415.10 Scalability, Modularity, and Future Directions 29515.11 Conclusions 29615.12 Key Definitions 29716 Applications in Agriculture and Food Systems 301N. Saheedha16.1 Introduction 30116.2 Image Processing in Food Systems 30816.3 Food Quality and Safety Assessment 30816.4 Integration with Machine Learning (ML) and Artificial Intelligence (AI) 31216.5 Challenges and Limitations 31416.6 Future Perspectives and Emerging Trends 31516.7 Conclusion 31517 Combining AI with Automated Quality Control in the Food Sector 321B. Leena, S. Karthikeyan, and Razaidi Hussin17.1 Introduction 32117.2 Literature Survey 32417.3 System Design 32617.4 Result and Discussion 32917.5 Conclusion 33318 Future Trends and Opportunities in Visualizing Nutrition 337Pushkar Singh Rawat and Shalini Singh18.1 Introduction 33718.2 Technological Advancements Shaping Nutrition Visualization 33918.3 Emerging Visualization Modalities 34018.4 Personalized and Precision Nutrition Visualization 34218.5 Applications Across Health, Policy, and Industry 34418.6 Challenges and Ethical Considerations 34518.7 Future Directions and Research Opportunities 34718.8 Conclusion 349References 350Index 355