http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Computer vision-based agriculture engineering
Machine generated contents note: ch. 1 Detecting Anatoxin 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 Anatoxin in Agricultural Products -- 1.3.1.Corn or Maize -- 1.3.1.1.Detection of Anatoxin by Hyperspectral Images -- 1.3.1.2.Detection of Anatoxin 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 -- 1.3.2.2.Pistachio Nuts, Hazelnuts, Brazil Nuts, and Peanuts -- 1.3.2.3.Chili Pepper -- 1.4.Limitation and Future Trends -- 1.4.1.Limitation -- 1.4.2.Future Trends -- 1.5.Conclusions -- References -- ch. 2 Anatoxin 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 -- References -- ch. 3 Application-Driven Key Wavelength Mining Method for Anatoxin 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 -- ch. 4 Deep Learning-Based Anatoxin Detection of Hyperspectral Data -- 4.1.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.Anatoxin Detection Using Key Band Images -- 4.3.2.Anatoxin Detection Using Spectral and Images -- 4.4.Conclusion -- References -- ch. 5 Pixel-Level Anatoxin Detection Based on Deep Learning and Hyperspectral Imaging -- 5.1.Introduction -- 5.2.Materials and Methods -- 5.2.1.Peanut Sample Preparation -- 5.2.2.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 -- ch. 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 -- 6.2.3.The Appearance Characteristic Index of the Seed -- 6.2.4.The Establishment of the Recognition Model -- 6.3.Results and Analysis -- 6.3.1.The Result of Recognition on Peanut Varieties -- 6.3.2.The Result of Recognition on Peanut Qualities -- 6.4.Analysis of the Results of Recognition and Detection -- 6.5.Discussions -- References -- ch. 7 Quality Grade Testing of Peanut Based on Image Processing -- 7.1.Introduction -- 7.2.Materials and Method -- 7.2.1.Materials for Testing -- 7.2.2.Maintaining the Integrity of the Specifications -- 7.2.3.Model of Quality Recognition -- 7.2.4.Method of Grading -- 7.3.Results and Analysis -- 7.3.1.Analysis of Recognition Results of Grains' Quality -- 7.3.2.Analysis of the Result of Specification and Grading -- 7.4.Conclusions -- References -- ch. 8 Study on Origin Traceability of Peanut Pods Based on Image Recognition -- 8.1.Introduction -- 8.2.Materials and Method -- 8.2.1.Test Materials -- 8.2.2.Methods -- 8.2.3.The Method Used to Optimize -- 8.3.Results and Analysis -- 8.4.Discussions -- 8.5.Conclusion -- References -- ch. 9 Study on the Pedigree Clustering of Peanut Pod's Variety Based on Image Processing -- 9.1.Introduction -- 9.2.Materials and Method -- 9.2.1.Experimental Materials -- 9.2.2.Methods -- 9.2.2.1.PCA Algorithm -- 9.2.2.2.Clustering Algorithm -- 9.3.Conclusion and Analysis -- 9.3.1.Statistical Characteristics Clustering -- 9.3.2.PCA Clustering -- 9.4.Discussions -- 9.5.Conclusions -- References -- ch. 10 Image Features and DUS Testing Traits for Identification and Pedigree Analysis of Peanut Pod Varieties -- 10.1.Introduction -- 10.2.Materials and Method -- 10.1.1.Materials -- 10.1.1.1.Peanut Samples -- 10.1.1.2.Image Acquisition -- 10.1.2.Methods -- 10.1.2.1.Feature Extraction -- 10.1.2.2.Analysis and Identification Model -- 10.3.Results and Analysis -- 10.3.1.Feature Selection by Fisher -- 10.3.2.Variety Identification by SVM -- 10.3.3.Paternity Analysis by E-Means -- 10.4.Discussions -- 10.4.1.Biological Basis for Seed Testing with Appearance -- 10.4.2.Finding Candidate Features for DUS Testing -- 10.5.Conclusions -- References -- ch. 11 Counting Ear Rows in Maize Using Image Processing Method -- 11.1.Introduction -- 11.2.Materials -- 11.3.Procedure -- 11.3.1.Image Obtaining -- 11.3.2.Characteristic Indicators of Corn Varieties -- 11.3.3.Pretreatment -- 11.3.4.Construction of the Counting Model -- 11.4.Results and Analysis -- 11.5.Conclusions -- References -- ch. 12 Single-Seed Precise Sowing of Maize Using Computer Simulation -- 12.1.Introduction -- 12.2.Materials and Methods -- 12.2.1.Mathematical Depiction of the Problem -- 12.2.2.Method of Analog Simulation -- 12.2.2.1.Computer Simulation of Planting Method -- 12.2.2.2.Seedling Missing Spots and Missing Seedling Compensation -- 12.3.Results and Analysis -- 12.3.1.Comparison of the Two Planting Methods' Yield -- 12.3.2.Influence of Field Seedling Emergence Rate on Yield -- 12.3.3.Interactions between Sensitivity and Field Seedling Emergence Rate -- 12.3.4.Seedling Missing Spots and Its Distribution Rule -- 12.4.Conclusion -- References -- ch. 13 Identifying Maize Surface and Species by Transfer Learning -- 13.1.Introduction -- 13.2.Materials and Method -- 13.2.1.Image Characteristics -- 13.2.2.Workflow Diagram -- 13.2.3.Convolutional Neural Network -- 13.2.4.Transfer Learning -- 13.3.Performance of the Model -- 13.4.Performance of Limited Data Model -- 13.5.Comparison with Manual Method -- 13.6.Expanding Application -- 13.7.Discussion -- 13.8.Conclusions and Future Work -- References -- ch. 14 A Carrot Sorting System Using Machine Vision Technique -- 14.1.Introduction -- 14.2.Materials and Methods -- 14.2.1.Carrot Samples -- 14.2.2.Grading System -- 14.2.3.Image Processing and Detection Algorithms -- 14.2.3.1.Image Preprocessing and Segmentation -- 14.2.3.2.Shape Detection Algorithm -- 14.2.3.3.Fibrous Root Detection -- 14.2.3.4.Surface Crack Detection -- 14.2.4.Bayes Classifier -- 14.3.Experimental Results -- 14.3.1.Detection -- 14.3.2.Fibrous Root Detection -- 14.3.3.Crack Detection -- 14.3.4.Time Efficiency -- 14.4.Discussions -- 14.4.1.Shape Detection -- 14.4.2.Fibrous Root Detection -- 14.4.3.Crack Detection -- 14.4.4.Future Work -- 14.5.Conclusions -- References -- ch. 15 A New Automatic Carrot Grading System Based on Computer Vision -- 15.1.Introduction -- 15.2.Materials and Methods -- 15.2.1.Materials -- 15.2.2.Design of Carrot Grading System -- 15.2.3.Image Processing and Grading Algorithms -- 15.2.3.1.Image Acquisition -- 15.2.3.2.Image Preprocessing -- 15.2.3.3.Defect Detection Algorithms -- 15.2.3.4.Grading Regular Carrots by Size -- 15.2.4.Control of Grading System -- 15.3.Results -- 15.3.1.Defect Detection -- 15.3.2.Regular Carrot Grading -- 15.3.3.Time Efficiency -- 15.3.4.Performance Parameter -- 15.4.Discussion -- 15.5.Conclusion -- References -- ch. 16 Identifying Carrot Appearance Quality by Transfer Learning -- 16.1.Introduction -- 16.2.Materials and Method -- 16.2.1.Image Characteristics -- 16.2.2.Workflow Diagram -- 16.2.3.Convolutional Neural Network -- 16.2.4.Transfer Learning -- 16.3.Performance of the Model -- 16.4.Comparison with Manual Work -- 16.5.An Expanding Application -- 16.6.Discussion -- 16.7.Conclusions and Future Work -- References -- ch. 17 Grading System of Pear's Appearance Quality Based on Computer Vision -- 17.1.Introduction -- 17.2.System Development -- 17.2.1.Formation of Grading System's Hardware Device -- 17.2.2.Concrete Implement -- 17.2.3.Testing of Real Object -- 17.2.4.Software System -- 17.3.Implementation of Algorithm -- 17.3.1.Detection of Defects -- 17.3.2.Feature Extraction -- 17.3.3.Grade Judgment -- 17.4.Results and Discussion -- 17.5.Ending Words -- References -- ch. 18 Study on Defect Extraction of Pears with Rich Spots and Neural Network Grading Method -- 18.1.Introduction -- 18.2.Material and Method -- 18.2.1.Experimental Materials -- 18.2.2.Image Preprocessing -- 18.2.2.1.Background Removing and Outline Extraction -- 18.2.2.2.Removal of Spots on the Surface -- 18.2.2.3.Extraction of Defective Parts -- 18.2.3.Feature Extraction and Recognition -- 18.2.3.1.Scalarization of National Standard -- 18.2.3.2.Extraction of Fruit Type and Defective Part -- 18.2.3.3.Judgment of ANN Grade -- 18.3.Results and Analysis -- 18.3.1.Effect of Spot Removal and Defect Extraction -- 18.3.2.Grade Judgment of Fruit Type and Defect -- 18.3.3.Comprehensive Grade Judgments -- 18.3.4.Influencing Factors of Grading of Comprehensive Quality -- 18.4.Discussions -- References -- ch. 19 Food Detection Using Infrared Spectroscopy with k-ICA and k-SVM: Variety, Brand, Origin, and Adulteration -- 19.1.Introduction -- 19.2.Materials and Methods -- 19.2.1.Materials -- 19.2.2.Algorithm -- 19.2.3.Flowchart -- 19.3.Results and Discussion -- 19.3.1.Different Features Selection Method -- 19.3.2.Different Recognition Models -- 19.3.3.Different Samples or Features -- 19.4.Summary -- References
Note continued: ch. 20 Study on Vegetable Seed Electrophoresis Image Classification Method -- 20.1.Introduction -- 20.2.Material and Method -- 20.2.1.Experimental Materials -- 20.2.2.Method -- 20.3.Results and Analysis -- 20.3.1.Recognition of Corp -- 20.3.2.Cluster Analysis -- 20.4.Discussions -- 20.5.Conclusions -- References -- ch. 21 Identifying the Change Process of a Fresh Pepper by Transfer Learning -- 21.1.Introduction -- 21.2.Materials and Method -- 21.2.1.Image Characteristics -- 21.2.2.Workflow Diagram -- 21.2.3.Convolutional Neural Network -- 21.2.4.Transfer Learning -- 21.3.The Performance of the Model -- 21.4.Computer vs Humans -- 21.5.Expanding Application -- 21.6.Discussion -- 21.7.Conclusions and Future Work -- References -- ch. 22 Identifying the Change Process of Fresh Banana by Transfer Learning -- 22.1.Introduction -- 22.2.Materials and Method -- 22.2.1.Image Dataset -- 22.2.2.Convolutional Neural Network -- 22.2.3.Transfer Learning -- 22.2.4.Experimental Setup -- 22.3.The Performance of Model -- 22.3.1.Experimental Result -- 22.3.2.Computer vs Humans -- 22.3.3.Expanding Application -- 22.4.Discussion -- 22.5.Conclusions -- References -- ch. 23 Pest Recognition Using Transfer Learning -- 23.1.Introduction -- 23.2.Materials and Methods -- 23.2.1.Materials -- 23.2.2.Introduce of the Model -- 23.3.Result and Analysis -- 23.3.1.Pests Recognition Result by Transfer Learning Model -- 23.3.2.Comparison of the Model with Traditional Methods -- 23.3.3.Comparison of the Model with Human Expert -- 23.3.4.Universal of the Transfer Learning Model -- 23.4.Discussion -- 23.4.1.Image Numbers and Image Capture Environment -- 23.4.2.Image Background and Segmentation -- 23.4.3.Similar Outline Disturb -- 23.5.Conclusion -- References -- ch. 24 Using Deep Learning for Image-Based Plant Disease Detection -- 24.1.Introduction -- 24.2.Materials and Methods -- 24.2.1.Dataset -- 24.2.2.Introduced Model -- 24.2.3.Experiment Environment -- 24.2.4.Experiment Result -- 24.3.Model Universal Adaptability -- 24.3.1.Big Datasets Validation -- 24.3.2.Small Datasets Validation -- 24.3.3.Artificial Recognition -- 24.4.Discussion -- 24.4.1.Effect of Data Size -- 24.4.2.Image Background -- 24.4.3.Symptom Variations -- 24.4.4.Machine Recognition and Human Recognition -- References -- ch. 25 Research on the Behavior Trajectory of Ornamental Fish Based on Computer Vision -- 25.1.Introduction -- 25.2.Experimental Materials and Methods -- 25.2.1.The Experimental Device -- 25.2.2.Preprocessing -- 25.2.3.The Positioning of the Fish -- 25.2.4.Reduction of Actual 3D Coordinates -- 25.3.Three-Dimensional Trajectory Analysis -- 25.4.Discussion -- 25.5.Conclusion -- References.
Includes bibliographical references and index.
New York, NY : CRC Press, Taylor & Francis Group, [2020]