02601    a2200301   4500001001100000005001700011008004200028020001800070037003600088040000700124041000800131072001500139072001500154072001300169072001300182072002100195072002100216072002100237072002100258072002000279100001700299245005000316250000600366260002400372300001000396520188000406999001302286103208921020250317100355.0250312042021xx        162           eng    a9781032089218  bTaylor & FranciscGBP 46.99fBB  a01  aeng7 aTVB2thema7 aRGC2thema7 aTVB2bic7 aRGC2bic7 aTEC0360002bisac7 aSCI0110002bisac7 aSCI0860002bisac7 aTEC0030002bisac7 a631.52332bisac1 aHan Zhongzhi10aComputer Vision-Based Agriculture Engineering  a1  bCRC Pressc20210630  a348 p  bIn 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.  c582d582