Overview
- Assists readers to solve the traditional problems of pratacultural science through computer science
- Focuses on intrinsic feature extraction of easily acquired common grass images instead of remote sensing images
- Realizes auto recognition of forage and microscope images mosaic by new application of artificial intelligence
- Inspires more investigators to get involved into the work of grassland digitization based on computer vision
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Table of contents (8 chapters)
Keywords
About this book
This book mainly deals with grassland digitalization and recognition through computer vision, which will make contributions to implement of grass auto recognition and data acquisition. Taking advantage of computer vision, it focuses on intrinsic feature extraction to realize the functions such as auto recognition of forage seeds and microscope images mosaic. The book presents a new approach for identification of grass seeds, with clear figures and detailed tables. It enlightens reader by solving the traditional problems of pratacultural science through the aid of computer science.
Authors and Affiliations
About the authors
Bibliographic Information
Book Title: Computer Vision based Identification and Mosaic of Gramineous Grass Seeds
Authors: Xin Pan, Xuanhe Zhao, Weihong Yan, Jiangping Liu, Xiaoling Luo, Tana Wuyun
DOI: https://doi.org/10.1007/978-981-16-3501-4
Publisher: Springer Singapore
eBook Packages: Earth and Environmental Science, Earth and Environmental Science (R0)
Copyright Information: China Agricultural Science and Technology Press 2021
Hardcover ISBN: 978-981-16-3500-7Published: 18 August 2021
Softcover ISBN: 978-981-16-3503-8Published: 19 August 2022
eBook ISBN: 978-981-16-3501-4Published: 17 August 2021
Edition Number: 1
Number of Pages: IX, 128
Number of Illustrations: 12 b/w illustrations, 78 illustrations in colour
Additional Information: Jointly co-published with China Agricultural Science and Technology Press, Beijing, China
Topics: Monitoring/Environmental Analysis, Computer Imaging, Vision, Pattern Recognition and Graphics, Artificial Intelligence, Agriculture