| Description |
1 online resource (349 pages) |
| Contents |
Cover; Half Title; Title Page; Copyright Page; Contents; Preface; Author; Chapter 1 Detecting Aflatoxin in Agricultural Products by Hyperspectral Imaging: A Review; 1.1 Introduction; 1.2 Main Detecting Methods; 1.2.1 Hyperspectral Imaging (HSI); 1.2.2 Near-Infrared Spectroscopy (NIRS); 1.2.3 Flow Chart Showing Typical Steps; 1.3 Detection of Aflatoxin in Agricultural Products; 1.3.1 Corn or Maize; 1.3.1.1 Detection of Aflatoxin by Hyperspectral Images; 1.3.1.2 Detection of Aflatoxin by Other Methods; 1.3.1.3 Detect Other Fungi; 1.3.2 Cereals, Nuts, and Others; 1.3.2.1 Wheat, Barley, and Rice |
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1.3.2.2 Pistachio Nuts, Hazelnuts, Brazil Nuts, and Peanuts1.3.2.3 Chili Pepper; 1.4 Limitation and Future Trends; 1.4.1 Limitation; 1.4.2 Future Trends; 1.5 Conclusions; References; Chapter 2 Aflatoxin Detection by Fluorescence Index and Narrowband Spectra Based on Hyperspectral Imaging; 2.1 Introduction; 2.2 Experiment Materials; 2.2.1 Sample Preparation and Image Acquisition; 2.2.2 Illumination Compensation and Kernel Segmentation; 2.3 Data Processing and Result Analysis; 2.3.1 Fluorescence Index; 2.3.2 Recognition and Regression; 2.3.3 Narrowband Spectra; 2.4 Discussion; 2.5 Conclusions |
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Chapter 4 Deep Learning-Based Aflatoxin Detection of Hyperspectral Data4.1 Introduction; 4.2 Materials and Methods; 4.2.1 Peanut Sample Preparation; 4.2.2 Hyperspectral Imaging System and Image Acquisition; 4.2.3 Hyperspectral Imaging Preprocessing; 4.2.4 CNN of Deep Learning Method; 4.3 Results and Discussion; 4.3.1 Aflatoxin Detection Using Key Band Images; 4.3.2 Aflatoxin Detection Using Spectral and Images; 4.4 Conclusion; References; Chapter 5 Pixel-Level Aflatoxin Detection Based on Deep Learning and Hyperspectral Imaging; 5.1 Introduction; 5.2 Materials and Methods |
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5.2.1 Peanut Sample Preparation5.2.2 Hyperspectral Imaging System and Image Acquisition; 5.2.3 Hyperspectral Imaging Preprocessing; 5.2.4 CNN of Deep Learning Method; 5.3 Results and Discussion; 5.3.1 Deep Learning for Training Kernels; 5.3.2 Deep Learning for Testing Kernels; 5.3.3 Models Compared for All Kernels; 5.4 Discussion; 5.5 Conclusions; References; Chapter 6 A Method of Detecting Peanut Cultivars and Quality Based on the Appearance Characteristic Recognition; 6.1 Introduction; 6.2 Materials and Method; 6.2.1 Materials for Test; 6.2.2 Image Acquisition and Pretreatment |
| Bibliography |
ReferencesChapter 3 Application-Driven Key Wavelength Mining Method for Aflatoxin Detection Using Hyperspectral Data; 3.1 Introduction; 3.2 Materials; 3.2.1 Experiment Materials; 3.2.2 System Integration; 3.3 Methods; 3.3.1 Data Preprocessing; 3.3.2 Recognition Methods; 3.4 Results; 3.4.1 Hyperspectral Wave by ASD; 3.4.2 Multispectral Images by Liquid Crystal Tunable Filter (HTLF) ; 3.4.3 Hyperspectral Images by GSM; 3.5 Discussion; 3.5.1 Key Wavelengths Selected by Weighted Voting; 3.5.2 Sorter Design; 3.6 Conclusion; References |
| Notes |
6.2.3 The Appearance Characteristic Index of the Seed |
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Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force. WlAbNL |
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Print version record |
| Subject |
Agricultural engineering -- Technological innovations
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Agriculture -- Remote sensing
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Quality control -- Optical methods.
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Agriculture -- Remote sensing
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Quality control -- Optical methods
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| Form |
Electronic book
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| ISBN |
9781000691610 |
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1000691616 |
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9781000691955 |
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1000691950 |
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