在中国东北部黑土地区,使用带有注意力机制和遥感的深度学习模型估计玉米产量
Xingke Li1,2, Yunfeng Lyu3, Bingxue Zhu4
1School of Geographic Science, Changchun Normal University, Changchun, 130102, China.
Scientific reports
|April 15, 2025
概括
这项研究引入了用于准确预测玉米产量的深度学习模型,并纳入了农业现代化因素. 该模型使用历史数据提供早期预测,有助于作物管理.
科学领域:
- 农业科学 农业科学
- 机器学习 机器学习
- 遥感 遥感 遥感 遥感
背景情况:
- 准确的玉米产量预测对于粮食安全和农业规划至关重要.
- 传统的方法往往缺乏准确性,无法解释现代农业实践.
研究的目的:
- 开发一种新的深度学习框架 (CNNAtBiGRU),用于精确估计玉米产量.
- 将人为因素纳入产量预测模型.
- 为了实现早期玉米产量预测.
主要方法:
- 开发了一个深度学习框架 (CNNAtBiGRU) 集成1D-CNN,BiGRU和注意力机制.
- 该模型应用于中国东北部的黑土地区.
- 除了传统数据外,还包括了人类造成的因素,如种植机械化程度 (DCM).
主要成果:
- 该CNNAtBiGRU模型实现了高准确度 (R2 = 0.896) 和玉米产量预测的稳定性.
- 增强植被指数 (EVI),太阳诱导的叶绿素光 (SIF) 和DCM被确定为关键预测因素.
- 该框架允许在没有未来天气预报的情况下提前1-2个月预测产量.
结论:
- 该CNNAtBiGRU框架在玉米产量预测的准确性和及时性方面取得了重大进展.
- 纳入人为因素提高了模型性能,反映了农业现代化.
- 早期产量预测能力为农业决策提供了宝贵的领先时间.
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