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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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BO-CNN-BiLSTM深度学习模型集成多源遥感数据,以改进冬季小麦产量估计.

Lei Zhang1, Changchun Li1, Xifang Wu1

  • 1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China.

Frontiers in plant science
|January 6, 2025
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概括

准确的冬季小麦产量估计对于粮食安全至关重要. 一个新的深度学习模型整合了太阳诱导的叶绿素光 (SIF) 和遥感数据,可以准确地预测收获前几周的产量.

关键词:
一维卷积神经网络 (1D CNN)贝叶斯优化 (BO) 是一个贝叶斯优化.双向的长期短期记忆 (BiLSTM)太阳引起的叶绿素光 (SIF)收益率估计收益率估计

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科学领域:

  • 农业科学 农业科学
  • 遥感 遥感 遥感 遥感
  • 数据科学数据科学数据科学

背景情况:

  • 准确的冬季小麦产量估计对于农业政策和粮食安全至关重要,尤其是在气候变化中.
  • 遥感和深度学习为作物监测和产量预测提供了先进的工具.
  • 太阳引起的叶绿素光 (SIF) 对作物光合作用监测有希望,但需要进一步探索以估计产量.

研究的目的:

  • 开发和评估一个深度学习模型,以准确估计冬季小麦产量.
  • 调查将SIF数据与传统遥感和气候数据集成的有效性.
  • 探索开发的模型在预测作物生长不同阶段的产量方面的能力.

主要方法:

  • 开发了一个深度学习模型,贝叶斯优化-卷积神经网络-双向长期短期记忆 (BO-CNN-BiLSTM或BCBL).
  • 该模型整合了传统的遥感变量 (TS),太阳引起的叶绿素光 (SIF) 和气候数据.
  • 贝叶斯优化 (BOM) 用于超参数调整以优化模型性能.

主要成果:

  • 集成TS,气候和SIF数据的BCBL模型实现了高估计准确性 (R2=0.81,RMSE=616.99公斤/公,MRE=7.14%).
  • 该模型准确地确定了冬季小麦产量形成的关键时期 (三月初至五月初).
  • 在收获前大约25天实现了高产量估计准确性,证明了模型的稳定性和通用性.

结论:

  • 与SIF数据相结合的BCBL模型提供了可靠的冬季小麦产量估计.
  • 这种方法为农业政策制定和现场管理提供了重大潜力.
  • 该研究强调了SIF数据在增强作物产量预测模型中的价值.