Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model

Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model

Accurate prediction of soil properties is essential for sustainable land management and precision agriculture. This study presents an LSTM-CNN-Attention model that integrates temporal and spatial feature extraction with attention mechanisms to improve predictive accuracy. Utilizing the LUCAS soil da...

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Bibliographic Details
Main Authors: Yiqiang Liu, Luming Shen, Xinghui Zhu, Yangfan Xie, Shaofang He
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Applied Sciences
Subjects:
LUCAS
hyperspectral data
Vis-NIR spectroscopy
soil properties prediction
deep learning
attention mechanism
Online Access:https://www.mdpi.com/2076-3417/14/24/11687
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