使用机器学习模型估计与水产量相关的特征,该模型整合了超光谱和纹理特征
Yufen Zhang1,2, Feifei Zhu1, Kaiming Liang1
1Rice Research Institute, Guangdong Academy of Agricultural Sciences/Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs/Guangdong Key Laboratory of Rice Science and Technology, Guangzhou, China.
Frontiers in plant science
|November 24, 2025
概括
精确的米属性估计使用光谱和纹理数据与机器学习. 这种优化的方法显著提高了叶子,叶面积,生物质和谷物产量的预测准确性.
科学领域:
- 农业科学 农业科学
- 植物现象学 植物现象学
- 遥感 遥感 遥感 遥感
背景情况:
- 准确的现象诊断依赖于对多种特征指标的快速,精确估计.
- 开发与水产量相关的特征 (叶子度 - LNC,叶面积指数 - LAI,地表生物质 - AGB,谷物产量 - GY) 的高级估计模型至关重要.
- 结合光谱数据,纹理数据,缩小维度和机器学习的策略是提高估计准确性的关键.
研究的目的:
- 提高关键产相关特征指标估计模型的准确性.
- 研究将光谱和纹理数据与缩小维度和机器学习技术相结合的有效性.
- 为准确诊断大米特征提供一个强大的方法.
主要方法:
- 超光谱树冠图像和特征数据 (LNC,LAI,AGB,GY) 在2022-2023年间同步收集.
- 应用了尺寸缩小技术 (皮尔森相关,SPA,CARS) 来选择敏感的波长.
- 使用ANN,SVM,1D-CNN和LSTM构建估计模型,并结合了光谱和纹理特征.
主要成果:
- 该SPA-ANN模型显示,对LNC (R2=0.82) 和LAI (R2=0.75) 的最佳预测.
- 对于AGB (R2=0.90) 和GY (R2=0.63) 来说,CARS-ANN模型是最佳的.
- 整合纹理特征提高了R2高达9.9%,并减少了RMSE高达27.2%.
结论:
- 优化的"光谱+纹理+缩小维度+机器学习"方法显著提高了大米特征估计模型的准确性.
- 这种方法为准确诊断与水产量相关的特征提供了有价值的科学基础和技术数据.
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