|
|
|
|
| LEADER |
02825nmm a2200409 u 4500 |
| 001 |
EB002016253 |
| 003 |
EBX01000000000000001179152 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
220613 ||| eng |
| 020 |
|
|
|a 9789811920271
|
| 100 |
1 |
|
|a Zhang, Zhao
|e [editor]
|
| 245 |
0 |
0 |
|a Unmanned Aerial Systems in Precision Agriculture
|h Elektronische Ressource
|b Technological Progresses and Applications
|c edited by Zhao Zhang, Hu Liu, Ce Yang, Yiannis Ampatzidis, Jianfeng Zhou, Yu Jiang
|
| 250 |
|
|
|a 1st ed. 2022
|
| 260 |
|
|
|a Singapore
|b Springer Nature Singapore
|c 2022, 2022
|
| 300 |
|
|
|a V, 136 p. 68 illus., 60 illus. in color
|b online resource
|
| 505 |
0 |
|
|a Applications of UAVs and machine learning in agriculture -- Robot Operating System Powered Data Acquisition for Unmanned Aircraft Systems in Digital Agriculture -- Unmanned aerial vehicle (UAV) applications in cotton production -- Time effect after initial wheat lodging on plot lodging ratio detection using UAV imagery and deep learning -- UAV mission height effects on wheat lodging ratio detection -- Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on An Optimized Hybrid Task Cascade Model -- UAV multispectral remote sensing for yellow rust mapping: opportunities and challenges -- Corn Goss's Wilt disease assessment based on UAV imagery
|
| 653 |
|
|
|a Machine learning
|
| 653 |
|
|
|a Machine Learning
|
| 653 |
|
|
|a Botany
|
| 653 |
|
|
|a Agronomy
|
| 653 |
|
|
|a Signal, Speech and Image Processing
|
| 653 |
|
|
|a Robotics
|
| 653 |
|
|
|a Robotic Engineering
|
| 653 |
|
|
|a Agriculture
|
| 653 |
|
|
|a Signal processing
|
| 653 |
|
|
|a Plant Science
|
| 700 |
1 |
|
|a Liu, Hu
|e [editor]
|
| 700 |
1 |
|
|a Yang, Ce
|e [editor]
|
| 700 |
1 |
|
|a Ampatzidis, Yiannis
|e [editor]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b Springer
|a Springer eBooks 2005-
|
| 490 |
0 |
|
|a Smart Agriculture
|
| 028 |
5 |
0 |
|a 10.1007/978-981-19-2027-1
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-981-19-2027-1?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 630
|
| 520 |
|
|
|a This book, consisting of 8 chapters, describes the state-of-the-art technological progress and applications of unmanned aerial vehicles (UAVs) in precision agriculture. It focuses on the UAV application in agriculture, such as crop disease detection, mid-season yield estimation, crop nutrient status, and high-throughput phenotyping. Different from individual papers focusing on a specific application, this book provides a holistic view for readers with a wide range of subjects. In addition to researchers in the areas of plant science, plant pathology, breeding, engineering, it is also intended for undergraduates and graduates who are interested in imaging processing, artificial intelligence in agriculture, precision agriculture, agricultural automation, and robotics
|