With rapid progress being made in both theory and practical applications, Artificial Intelligence (AI) is transforming every aspect of life and leading the world towards a sustainable future. AI technology is fundamentally and radically affecting agriculture with a move towards smart systems. The outcome of this transition is improved efficiency, reduced environmental pollution, and enhanced productivity of crops.Nondestructive Evaluation of Agro-products by Intelligent Sensing Techniques is a reference which provides readers timely updates in the progress of intelligent sensing techniques used for nondestructive evaluation of agro-products. Chapters, each contributed by experts in food safety and technology, describe existing and innovative techniques that could be or have been applied to agro-products quality and safety evaluation, processing, harvest, traceability, and so on. The book includes 11 individual chapters, with each chapter focusing on a specific aspect of intelligent sensing techniques applied in agriculture. Specifically, the first chapter introduces the reader to representative techniques and methods for nondestructive evaluation. Subsequent chapters present detailed information about the processing and quality evaluation of agro-products (e.g., fruits, and vegetables), food grading, food tracing, and the use of robots for harvesting specialty crops.Key Features:- 11 chapters, contributed by experts that cover basic and applied research in agriculture- introduces readers to nondestructive evaluation techniques- covers food quality evaluation processes- covers food grading and traceability systems- covers frontier topics that represent future trends (robots and UAVs used in agriculture)- familiarizes the readers with several intelligent sensing technologies used in the agricultural sector (including machine vision, near-infrared spectroscopy, hyperspectral/multispectral imaging, bio-sensing, multi-technology fusion detection)- provides bibliographic references for further reading- gives applied examples on both common and specialty cropsThis reference is intended as a source of updated information for consultants, students and academicians involved in agriculture, crops science and food biotechnology. Professionals involved in food safety and security planning and policymaking will also benefit from the information presented by the authors.
Author(s): Jiangbo Li, Zhao Zhang
Publisher: Bentham Science Publishers
Year: 2021
Language: English
Pages: 313
City: Singapore
Cover
Title
Copyright
End User License Agreement
Contents
Preface
List of Contributors
Representative Techniques and Methods for Nondestructive Evaluation of Agro-products
Dong Hu1, Tong Sun1,* and Jiangbo Li2,*
1. INTRODUCTION
2. EMERGING NONDESTRUCTIVE TECHNIQUES
2.1. Near Infrared Spectroscopy
2.2. Infrared Spectroscopy
2.3. Fluorescence Spectroscopy
2.4. Raman Spectroscopy
2.5. Laser Induced Breakdown Spectroscopy
2.6. Traditional Machine Vision
2.7. Hyperspectral and Multispectral Imaging
2.8. Magnetic Resonance Imaging
2.9. X-ray Imaging
2.10. Thermal Imaging
2.11. Light Backscattering Imaging
2.12. Electrical Nose Technique
2.13. Acoustics Techniques
2.14. Other Techniques
3. FUTURE PERSPECTIVES
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Evaluation of Quality of Agro-Products by Imaging and Spectroscopy
Insuck Baek1, Jianwei Qin1, Byoung-Kwan Cho2 and Moon S. Kim1,*
1. INTRODUCTION
2. SPECTROSCOPY TECHNIQUES
2.1. Types of Spectroscopy
2.2. Spectroscopy Measurement
2.3. Data Preprocessing and Analysis
3. IMAGING TECHNIQUES
3.1. Illumination
3.2. Digitizer or Frame Grabber
3.3. Camera
3.4. Types of Imaging
4. APPLICATIONS
4.1. Fruits & Vegetables
4.2. Meats
4.3. Miscellaneous Applications
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Evaluation of Quality and Safety of Agro-products Based on Bio-sensing Technique
Lin Zhang and Yingchun Fu*
1. BRIEF INTRODUCTION OF BIO-SENSING TECHNIQUE FOR THE EVALUATION OF QUALITY AND SAFETY OF AGRO-PRODUCTS
2. ADVANCES IN BIOSENSORS FOR THE EVALUATION OF QUALITY AND SAFETY OF AGRO-PRODUCTS
2.1. Biosensors for Pesticide Residues
2.2. Biosensors for Antibiotic Residues
2.3. Biosensors for Pathogenic Bacteria and Mycotoxins
2.4. Biosensors for Heavy Metal Ions
2.5. Biosensors for Food Allergens
2.6. Biosensors for Ingredients
CONCLUSIONS AND PERSPECTIVES
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Internal Quality Grading Technologies and Applications for Agricultural Products
Aichen Wang1,*, Wen Zhang2 and Jiangbo Li3
1. INTRODUCTION
2. INTERNAL QUALITY GRADING TECHNOLOGIES AND APPLICATIONS
2.1. Vis/NIR Spectroscopy
2.1.1. Principle
2.1.2. Applications
2.1.3. Challenges and Perspectives
2.2. Multi-/Hyper-spectral Imaging
2.2.1. Principle
2.2.2. Application
2.2.3. Challenges and Perspectives
2.3. Nuclear Magnetic Resonance and Imaging
2.3.1. Principle
2.3.2. Applications
2.3.3. Challenges and Perspectives
2.4. X-ray and Computed Tomography
2.4.1. Principle
2.4.2. Applications
2.4.3. Challenges and Perspectives
2.5. Electrical Nose Technique
2.5.1. Principle
2.5.2. Signal Processing Methods
2.5.3. Applications
2.5.4. Challenges and Perspectives
2.6. Acoustic Technique
2.6.1. Principle
2.6.2. General Process of the Acoustic Vibration Method
2.6.3. Applications
2.6.4. Challenges and Perspectives
3. SUMMARY
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Hyperspectral Imaging and Machine Learning for Rapid Assessment of Deoxynivalenol of Barley Kernels
Wen-Hao Su1, Ce Yang1,*, Yanhong Dong2, Ryan Johnson2, Rae Page2, Tamas Szinyei2, Cory D. Hirsch2 and Brian J. Steffenson2
1. INTRODUCTION
2. MATERIALS AND METHODS
2.1. Samples Preparation
2.2. Instrumentation Systems
2.3. Machine Learning Algorithms
2.4. Analysis of Outliers
2.5. Model Assessment
3. RESULT AND DISCUSSION
3.1. Spectral Features
3.2. Outlier Evaluation
3.3. Classification of DON Levels
3.4. Quantitative Determination of DON Contents
4. DISCUSSION
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Evaluation of Fungal Contaminants in Agricultural Products by Hyperspectral Imaging
Feifei Tao1, Haibo Yao1,*, Zuzana Hruska1 and Kanniah Rajasekaran2
1. INTRODUCTION
1.1. Major Fungal Contaminants in Agricultural Products
2.2. HSI Technology
2. APPLICATION OF HSI IN EVALUATION OF FUNGAL CONTAMINANTS IN AGRICULTURAL PRODUCTS
2.1. Application of Fluorescence HSI
2.1.1. Evaluation of Aflatoxin Contamination
2.1.2. Evaluation of Other Major Fungal Contaminants
2.2. Application of Reflectance HSI
2.2.1. Evaluation of Aflatoxin and Related Fungal Contaminants
2.2.2. Evaluation of OTA and Related Fungal Contaminants
2.2.3. Evaluation of DON and Related Fungal Contaminants
2.2.4. Evaluation of Other Mycotoxins and Fungal Contaminants
CONCLUSION AND FUTURE OUTLOOK
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Intelligent Sensing Technology for Processing of Agro-products
Zhiming Guo1,2,*
1. INTRODUCTION
2. OPTICAL SENSING TECHNOLOGY IN AGRICULTURAL PRODUCTS PROCESSING
2.1. Principle of Optical Sensing Technology
2.2. Advances in Optical Sensor Technology
2.2.1. Application of Optical Sensor Technology in Agricultural Products Quality
2.2.2. Application of Optical Sensor Technology in Agricultural Products Safety
2.3. Typical Application of Optical Sensor Technology
2.3.1. An Effective Method to Inspect and Classify the Bruising Degree of Apples Based on the Optical Properties
2.3.2. Assessing Firmness and Ssc of Pears Based on Absorption and Scattering Properties using an Automatic Integrating Sphere System
2.4. The Future Trends of Optical Sensor Technology
3. ACOUSTIC SENSING TECHNOLOGY IN AGRICULTURAL PRODUCTS PROCESSING
3.1. Principle of Acoustic Sensing Technology
3.2. Research Advances and Application of Acoustic Sensing Technology
3.2.1. Acoustic Detection of Physical Characteristics of Agricultural Products
3.2.2. Ultrasound for Monitoring of Agricultural Products and Extending Shelf Life
3.2.3. Ultrasonic Techniques for the Recognition and Separation of Agricultural Products
3.2.4. The Future Trends of Acoustic Sensing Technology
4. ELECTRICAL SENSING TECHNOLOGY IN AGRICULTURAL PRODUCTS PROCESSING
4.1. Dielectric Properties of Agricultural Products Processing
4.1.1. Parallel Plate Capacitor
4.1.2. Resonant Cavity Method
4.2. Radio Frequency of Agricultural Products Processing
4.3. High Voltage Electric Field of Agricultural Products Processing
4.3. Electrical Resistance of Agricultural Products Processing
5. MAGNETIC SENSING TECHNOLOGY IN AGRICULTURAL PRODUCTS PROCESSING
5.1. Principle of Magnetic Sensing Technology
5.2. Research Advances and Application of Magnetic Sensing Technology
5.2.1. Magnetic Technology for Analysis of Internal Components and Defects of Agricultural Products
5.2.2. Magnetic Technology for Quality Control of Dairy Products
5.2.3. Magnetic Techniques for Detection of Lipid Deposition Patterns and Moisture in Agricultural Products
5.3. The Future Trends of Magnetic Sensing Technology
6. SENSORY SENSING TECHNOLOGY IN AGRO-PRODUCTS PROCESSING
6.1. Concept and Principle of Sensory
6.2. Electronic Nose
6.3. Colorimetric Sensors
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Automation on Fruit and Vegetable Grading System and Traceability
Devrim Ünay*
1. INTRODUCTION
2. METHODS
2.1. Automated Fruit-Vegetable Sorting
2.1.1. The Supermarket Produce Dataset
2.1.2. Proposed Deep Learning-based Sorting System
2.2. Automated Quality Inspection
2.2.1. The CAPA Dataset
2.2.2. Proposed Deep Learning-based Grading System
2.3. Experimental Evaluation
3. RESULTS
CONCLUDING REMARKS
Practical Use of the Proposed Solution
Traceability of the Proposed Solution
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Robotic Harvesting of Orchard Fruits
Fangfang Gao1 and Longsheng Fu1,2,3,4,*
1. INTRODUCTION
2. ROBOTIC HARVESTING OF APPLE
2.1. Fruit Detection for Apple
2.1.1. Single Feature Detection Methods
2.1.2. Multi-features Fusion Detection Methods
2.1.3. Deep Learning Methods
2.1.4. 3D Reconstruction Methods
2.2. Fruit Harvesting for Apple
2.2.1. End-effector Design for Selective Harvesting
2.2.2. Shake-and-catch for Bulk Harvesting
3. ROBOTIC HARVESTING OF KIWIFRUIT
3.1. Fruit Detection for Kiwifruit
3.2. End-effector for Kiwifruit
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Detection of Wheat Lodging Plots using Indices Derived from Multi-spectral and Visible Images
Zhao Zhang and Paulo Flores*
1. INTRODUCTION
2. MATERIALS AND METHODS
2.1. Field Experiments
2.2. Image Acquisition and Processing
2.3. Feature Extraction
2.3.1. RGB Color Feature
2.3.2. Texture Feature
2.3.3. NDVI Feature
2.3.4. Plant Height Feature
2.4. Classifier and Datasets Separation
3. RESULTS AND DISCUSSION
3.1. Color Feature Analysis
3.1.1. Texture Feature Analysis
3.1.2. NDVI Feature Analysis
3.1.3. Height Feature Analysis
3.2. SVM Training and Predicting
3.3. Identifying Feature Combinations with Desirable Prediction Accuracy
CONCLUSION
AUTHOR CONTRIBUTIONS
FUNDING
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Subject Index
