Computer Vision-Based Agriculture Engineering

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

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.