344 pages | 162 B/W Illus.
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 divided into six parts. It covers computer vision, image processing, hyperspectral imaging and other related technologies in peanut aflatoxin, peanut and corn quality varieties, carrot and potato quality, as well as pest and disease detection.
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 the book of all 25 chapters. This unique work provides student, engineers and technologists working in research, development, and operations in the agricultural engineering with critical, comprehensive and readily accessible information. The book applies development of artificial intelligence theory and methods including depth learning and transfer learning to the field of agricultural engineering testing.
1.Detecting aflatoxin in agricultural products by hyperspectral imaging:A Review 2.Aflatoxin Detecting by FluorescenceIndex andNarrowband Spectral based on Hyperspectral imaging 3.Application driven key wavelengths mining method for Aflatoxin detection using hyperspectral data 4.Deep Learning-Based Aflatoxin Detecting of Hyperspectral Data 5.Pixel-level Aflatoxin Detecting Based on Deep Learning and Hyperspectral Imaging 6.A Method of Detecting Peanut Cultivars and Quality Based on the Appearance Characteristic Recognition 7.Quality Grade-Testing of Peanut Based on Image Processing 8.Study on Origin Traceability of Peanut Pods Based on Image Recognition 9.Study On The Pedigree Clustering of Peanut Pod’s Variety based on Image Processing 10.Image features and DUS testing traits for peanut pods varieties identification and pedigree analysis 11.Counting Ear Rows in Maize Using Image Process Method 12.Single seed precise sowing in maize using computer simulation 13.Identifying maize surface and species by transfer learning 14.A Carrot Sorting System Using Machine Vision Technique 15.Automatic carrot grading system based on computer vision 16. Identifying Carrot Appearance Quality by Transfer Learning 17.Grading System of Pear’s Appearance Quality Based on Computer Vision 18.Study on Defect Extraction of Pears with Rich Spots and Neural Network Grading Method 19 .Food Detection using Infrared Spectroscopy with k-ICA and k-SVM：Variety, Brand, Origin and Adulteration 20.Study on Vegetable Seed Electrophoresis Image Classification Method 21. Identifying the change process of fresh pepper by transfer learning 22. Identifying the change process of fresh Banana by transfer learning 23. Pest Recognition Using Transfer Learning 24. Using Deep Learning for Image-Based Plant Disease Detection 25. Research on the behavior trajectory of ornamental fish based on computer vision