Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data

Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data

Accurate soybean yield estimation is critically imperative for modern agricultural systems amid escalating global food security pressures, yet conventional methodologies are constrained for large-scale high-frequency monitoring. To address this, an innovative deep learning framework, TransBiHGRU-PSO...

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Bibliografiska uppgifter
Huvudupphovsmän: Jian Li, Junrui Kang, Ji Qi, Jian Lu, Hongkun Fu, Baoqi Liu, Xinglei Lin, Jiawei Zhao, Hengxu Guan, Jing Chang, Zhihan Liu
Materialtyp: Artikel
Språk:engelska
Publicerad: IEEE 2025-01-01
Serie:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Ämnen:
Deep learning (DL)
greenness and water content composite index (GWCCI)
multisource remote sensing data
yield estimation
Länkar:https://ieeexplore.ieee.org/document/11079987/
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https://ieeexplore.ieee.org/document/11079987/

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