Computer Vision-Based Agriculture Engineering. Questo articolo non è disponibile.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.

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.