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

authored by

Mahdiyeh Fathi, Reza Shah-Hosseini, Armin Moghimi, Hossein Arefi

Abstract

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 do not fully consider multi-source RS data for prediction, as processing these increases complexity and limits their accuracy and generalizability. In this study, we propose the Multi-Residual Attention-Based Multi-Stream 3D-ResNet-BiLSTM (MHRA-MS-3D-ResNet-BiLSTM) model, designed to integrate various RS data types, including Sentinel-1/2 imagery, Daymet climate data, and soil grid information, for improved county-level U.S. soybean yield prediction. Our model employs a multi-stream architecture to process diverse data types concurrently, capturing complex spatio-temporal features effectively. The 3D-ResNet component utilizes 3D convolutions and residual connections for pattern recognition, complemented by Bidirectional Long Short-Term Memory (BiLSTM) for enhanced long-term dependency learning by processing data arrangements in forward and backward directions. An attention mechanism further refines the model’s focus by dynamically weighting the significance of different input features for efficient yield prediction. We trained the MHRA-MS-3D-ResNet-BiLSTM model using multi-source RS datasets from 2019 and 2020 and evaluated its performance with U.S. soybean yield data for 2021 and 2022. The results demonstrated the model’s robustness and adaptability to unseen data, achieving an R² of 0.82 and a Mean Absolute Percentage Error (MAPE) of 9% in 2021, and an R² of 0.72 and MAPE of 12% in 2022. This performance surpassed some of the state-of-the-art models like 3D-ResNet-BiLSTM and MS-3D-ResNet-BiLSTM, and other traditional ML methods like Random Forest (RF), XGBoost, and LightGBM. These findings highlight the methodology’s capability to handle multiple RS data types and its role in improving yield predictions, which can be helpful for sustainable agriculture.

Organisation(s)

Ludwig-Franzius-Institute of Hydraulics, Estuarine and Coastal Engineering

External Organisation(s)

University of Tehran
University of Applied Sciences Ludwigshafen am Rhein

Type

Article

Journal

Remote sensing

Volume

17

No. of pages

25

ISSN

2072-4292

Publication date

31.12.2024

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

General Earth and Planetary Sciences

Sustainable Development Goals

SDG 2 - Zero Hunger

Electronic version(s)

https://doi.org/10.3390/rs17010107 (Access: Open)