MHRA-MS-3D-ResNet-BiLSTM: A Multi-Head-Residual Attention-Based Multi-Stream Deep Learning Model for Soybean Yield Prediction in the U.S. Using Multi-Source Remote Sensing Data

MHRA-MS-3D-ResNet-BiLSTM: A Multi-Head-Residual Attention-Based Multi-Stream Deep Learning Model for Soybean Yield Prediction in the U.S. Using Multi-Source Remote Sensing Data

Accurate prediction of soybean yield is important for safeguarding food security and improving agricultural management. Recent advances have highlighted the effectiveness and ability of Machine Learning (ML) models in analyzing Remote Sensing (RS) data for this purpose. However, most of these models...

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Bibliographic Details
Main Authors: Mahdiyeh Fathi, Reza Shah-Hosseini, Armin Moghimi, Hossein Arefi
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Remote Sensing
Subjects:
soybean
yield prediction
Multi-Stream
Residual Attention
Multi-Head Attention
3D-ResNet-biLSTM
Online Access:https://www.mdpi.com/2072-4292/17/1/107
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https://www.mdpi.com/2072-4292/17/1/107

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