WE4.B2.1
A KNOWLEDGE-GUIDED DEEP LEARNING FRAMEWORK WITH REMOTELY SENSED VARIABLES AND METEOROLOGICAL VARIABLES FOR IMPROVING WHEAT YIELD ESTIMATION
Huiren Tian, Xi’an University of Science and Technology, China
Session:
WE4.B2: Advances in Yield and Production Estimation for Agricultural Policy and Food Security with Earth Observations II Oral
Track:
Community-Contributed Sessions
Location:
Boulevard: Room B2
Presentation Time:
Wednesday, 6 August, 15:45 - 16:00
Presentation
Discussion
Resources
No resources available.
Session WE4.B2
WE4.B2.1: A KNOWLEDGE-GUIDED DEEP LEARNING FRAMEWORK WITH REMOTELY SENSED VARIABLES AND METEOROLOGICAL VARIABLES FOR IMPROVING WHEAT YIELD ESTIMATION
Huiren Tian, Xi’an University of Science and Technology, China
WE4.B2.2: ATTENTION MECHANISM-BASED DEEP LEARNING APPROACH FOR WHEAT YIELD ESTIMATION AND UNCERTAINTY ANALYSIS FROM REMOTELY SENSED VARIABLES
Huiren Tian, Pengxin Wang, China Agricultural University, China; Kevin Tansey, University of Leicester, United Kingdom; Jie Wang, China Agricultural University, China; Wenting Quan, Shaanxi Provincial Meteorological Bureau, China; Junming Liu, China Agricultural University, China
WE4.B2.3: YIELDFUSION: ENHANCING YIELD PREDICTION WITH TRANSFER LEARNING
Anamika Dey, Somrita Sarkar, V. V. S. Sri Harsha, Chandranath Chatterjee, Indian Institute of Technology Kharagpur, India; Arijit Mondal, Indian Institute of Technology, Patna, India; Pabitra Mitra, Indian Institute of Technology Kharagpur, India
Resources
No resources available.