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提高大米产量预测:一个深度融合模型,将ResNet50-LSTM与多源数据集成在一起

Aqsa Aslam¹, Saima Farhan¹

¹Department of Computer Science, Lahore College for Women University, Lahore, Punjab, Pakistan.

PeerJ. Computer science

|September 24, 2024

概括

这项研究引入了一个深度学习模型 (ResNet50-LSTM),使用卫星数据和气候信息准确预测巴基斯坦的水产量. 混合模型显示高精度,有助于全球作物产量估计和粮食安全.

科学领域:

农业科学农业科学
计算机科学计算机科学
环境科学环境科学

背景情况:

全球粮食安全严重依赖于大米生产,这是巴基斯坦面临气候变化和流行病影响的关键作物.
准确的产预测对于巴基斯坦的经济稳定和知情决策至关重要.
现有的预测模型需要改进,以应对气候变化和流行病对作物产量的复杂性.

研究的目的:

开发和评估一种基于深度学习的创新混合预测模型,用于预测巴基斯坦的水产量.
利用多模式数据,包括卫星图像 (EVI,LAI,FPAR) 和气象/土壤数据,以提高预测准确度.
评估不同ResNet50-LSTM配置和功能组合的性能,以实现最佳的水产量预测.

主要方法:

开发了一种混合深度学习模型 (ResNet50-LSTM),集成ResNet50用于从卫星数据中提取特征,以及LSTM用于时间序列预测.
多模式数据,包括MODIS卫星指数 (EVI,LAI,FPAR),气象和土壤数据,使用谷歌地球引擎收集和预处理.
测试了三个具有不同层架构的LSTM模型配置,并分析了特征的重要性,以确定最佳预测因素.

主要成果:

具有两个相互连接的LSTM层的ResNet50-LSTM模型显示出卓越的预测性能.
该模型通过选择一组特征 (EVI,FPAR,气候和土壤变量) 实现了高精度,产生R2 = 0.9903和RMSE = 0.1854.
发现EVI和FPAR的组合对于预测水产量特别有效.

结论:

开发的ResNet50-LSTM框架为预测大米产量提供了强大而准确的方法.
该研究强调了利用公开可用的多来源数据来估计全球作物产量的潜力.
这种方法可以支持明智的农业决策,提高生产率,并有助于粮食安全.

关键词:

深度学习是一种深度学习.谷歌的地球引擎.这是NASA的POWER项目.遥感是一种远程传感.在 ResNet-LSTM 系统中使用 ResNet-LSTM.大米产量预测预测

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