In recent years, computer vision is a fast-growing technique of agricultural engineering, especially in quality detection of agricultural products and food safety testing. It can provide objective, rapid, non-contact and non-destructive methods by extracting quantitative information from digital images. Significant scientific and technological advances have been made in quality inspection, classification and evaluation of a wide range of food and agricultural products. Computer Vision-Based Agriculture Engineering focuses on these advances.
The book contains 25 chapters covering computer vision, image processing, hyperspectral imaging and other related technologies in peanut aflatoxin, peanut and corn quality varieties, and carrot and potato quality, as well as pest and disease detection.
Features:
Discusses various detection methods in a variety of agricultural crops
Each chapter includes materials and methods used, results and analysis, and discussion with conclusions
Covers basic theory, technical methods and engineering cases
Provides comprehensive coverage on methods of variety identification, quality detection and detection of key indicators of agricultural products safety
Presents information on technology of artificial intelligence including deep learning and transfer learning
Computer Vision-Based Agriculture Engineering is a summary of the author's work over the past 10 years. Professor Han has presented his most recent research results in all 25 chapters of this book. This unique work provides students, engineers and technologists working in research, development, and operations in agricultural engineering with critical, comprehensive and readily accessible information. It applies development of artificial intelligence theory and methods including depth learning and transfer learning to the field of agricultural engineering testing.
Preface
Author
Chapter 1 Detecting Aflatoxin in Agricultural Products by Hyperspectral Imaging: A Review
Chapter 2 Aflatoxin Detection by Fluorescence Index and Narrowband Spectra Based on Hyperspectral Imaging
Chapter 3 Application-Driven Key Wavelength Mining Method for Aflatoxin Detection Using Hyperspectral Data
Chapter 4 Deep Learning-Based Aflatoxin Detection of Hyperspectral Data
Chapter 5 Pixel-Level Aflatoxin Detection Based on Deep Learning and Hyperspectral Imaging
Chapter 6 A Method of Detecting Peanut Cultivars and Quality Based on the Appearance Characteristic Recognition
Chapter 7 Quality Grade Testing of Peanut Based on Image Processing
Chapter 8 Study on Origin Traceability of Peanut Pods Based on Image Recognition
Chapter 9 Study on the Pedigree Clustering of Peanut Pod’s Variety Based on Image Processing
Chapter 10 Image Features and DUS Testing Traits for Identification and Pedigree Analysis of Peanut Pod Varieties
Chapter 11 Counting Ear Rows in Maize Using Image Processing Method
Chapter 12 Single-Seed Precise Sowing of Maize Using Computer Simulation
Chapter 13 Identifying Maize Surface and Species by Transfer Learning
Chapter 14 A Carrot Sorting System Using Machine Vision Technique
Chapter 15 A New Automatic Carrot Grading System Based on Computer Vision
Chapter 16 Identifying Carrot Appearance Quality by Transfer Learning
Chapter 17 Grading System of Pear’s Appearance Quality Based on Computer Vision
Chapter 18 Study on Defect Extraction of Pears with Rich Spots and Neural Network Grading Method
Chapter 19 Food Detection Using Infrared Spectroscopy with k-ICA and k-SVM: Variety, Brand, Origin, and Adulteration
Chapter 20 Study on Vegetable Seed Electrophoresis Image Classification Method
Chapter 21 Identifying the Change Process of a Fresh Pepper by Transfer Learning
Chapter 22 Identifying the Change Process of Fresh Banana by Transfer Learning
Chapter 23 Pest Recognition Using Transfer Learning
Chapter 24 Using Deep Learning for Image-Based Plant Disease Detection
Chapter 25 Research on the Behavior Trajectory of Ornamental Fish Based on Computer Vision
Index
Biography
Han Zhongzhi (1981.6-), Ph. D., Male, Born in Junan County, Shandong Province, China. Full professor of Qingdao Agricultural University, Master's supervisor, 3-level candidate of "1361" talent engineering, head of modern agricultural intelligent equipment innovation team; Chief expert of Qingdao agricultural intelligent equipment expert workstation, Evaluation expert of National Natural Science Foundation and National IOT Special Fund, Intel Certified Visual Computing Engineer, member of International Computer Association (ACM) Expert Committee, Reviewer of many journals such as "Computers and Electronics in Agriculture" and Editorial Committee of "Higher Education Research and Practice". The main research area is computer vision intelligent detection in agricultural products.
