BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation

BO-CNN-BiLSTM deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation

IntroductionIn the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employe...

Full description

Saved in:
Bibliographic Details
Main Authors: Lei Zhang, Changchun Li, Xifang Wu, Hengmao Xiang, Yinghua Jiao, Huabin Chai
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-12-01
Series:Frontiers in Plant Science
Subjects:
bidirectional long short-term memory (BiLSTM)
1D convolutional neural network (1D CNN)
Bayesian optimization (BO)
solar-induced chlorophyll fluorescence (SIF)
yield estimation
Online Access:https://www.frontiersin.org/articles/10.3389/fpls.2024.1500499/full
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.frontiersin.org/articles/10.3389/fpls.2024.1500499/full

Similar Items