MS-YieldStackNet:使用堆叠合体神经网络进行小麦产量估计的多源数据融合
Waqas Ali1, Zeeshan Ramzan2, Muhammad Shahbaz3
1Department of Computer Science, University of Engineering and Technology Lahore, Lahore, Pakistan.
PeerJ. Computer science
|January 22, 2026
概括
这项研究介绍了MS-YieldStackNet,这是使用卫星数据和土壤分析准确预测小麦产量的新型框架. 该模型增强了巴基斯坦等地区的粮食安全和农业规划.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 准确的作物产量预测对于粮食安全和农业政策至关重要.
- 手动估计小麦产量的方法是劳动密集型和不精确的,特别是在巴基斯坦.
- 整合不同的数据源可以提高收益率预测的准确性.
研究的目的:
- 开发和验证一个新的算法框架,MS-YieldStackNet,用于高分辨率的小麦产量预测.
- 整合多光谱卫星图像,现场土壤分析和气象变量以提高预报.
- 用关键的统计指标来评估模型的性能.
主要方法:
- 使用植被指数 (NDVI,DVI),土壤物理化学属性和时间气候数据构建了一个统一的特征空间.
- 采用了一个堆叠集团神经架构 (MS-YieldStackNet),结合了三个并行前神经网络 (FFNNs).
- 使用随机森林元学习器来整合FFNNs的预测.
主要成果:
- 实现了0.81的强大的R平方值,表明了强大的模型性能.
- 报告的平均平方误差 (MSE) 为6114.30公斤/公,根的平均平方误差 (RMSE) 为78.19公斤/公.
- 预测误差很低,平均绝对误差 (MAE) 为59.07公斤/公,平均绝对百分比误差 (MAPE) 为3.55%.
结论:
- MS-YieldStackNet为小麦产量预测提供了一个精确且可扩展的解决方案.
- 与传统方法相比,综合方法显著提高了预测准确度.
- 该框架具有很大的潜力,可以为农业政策提供信息,并确保粮食安全.
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