您好，欢迎访问山东省农业科学院文献资源数据库平台

NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States

文献类型： 外文期刊

作者： He, Qinghai 1 ; Zhang, Haowen 2 ; Li, Tianhua 4 ; Zhang, Xiaojia 3 ; Li, Xiaoli 1 ; Dong, Chunwang 3 ;

作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310029, Peoples R China

2.Shandong Acad Agr Machinery Sci, Jinan 250100, Peoples R China

3.Shandong Acad Agr Sci, Tea Res Inst, Jinan 250100, Peoples R China

4.Shandong Agr Univ, Coll Mech & Elect Engn, Tai An 271000, Peoples R China

关键词： NIR spectroscopy; soil; dimensionality reduction; support vector regression; tea garden

期刊名称：SENSORS （ 影响因子：3.9； 五年影响因子：4.1 ）

ISSN：

年卷期： 2023 年 23 卷 22 期

页码：

收录情况： SCI

摘要： Soil fertility is vital for the growth of tea plants. The physicochemical properties of soil play a key role in the evaluation of soil fertility. Thus, realizing the rapid and accurate detection of soil physicochemical properties is of great significance for promoting the development of precision agriculture in tea plantations. In recent years, spectral data have become an important tool for the non-destructive testing of soil physicochemical properties. In this study, a support vector regression (SVR) model was constructed to model the hydrolyzed nitrogen, available potassium, and effective phosphorus in tea plantation soils of different grain sizes. Then, the successful projections algorithm (SPA) and least-angle regression (LAR) and bootstrapping soft shrinkage (BOSS) variable importance screening methods were used to optimize the variables in the soil physicochemical properties. The findings demonstrated that soil particle sizes of 0.25-0.5 mm produced the best predictions for all three physicochemical properties. After further using the dimensionality reduction approach, the LAR algorithm (R-C(2) = 0.979, R-P(2) = 0.976, RPD = 6.613) performed optimally in the prediction model for hydrolytic nitrogen at a soil particle size of 0.25 similar to 0.5. The models using data dimensionality reduction and those that used the BOSS method to estimate available potassium (R-C(2) = 0.977, R-P(2) = 0.981, RPD = 7.222) and effective phosphorus (R-C(2) = 0.969, R-P(2) = 0.964, RPD = 5.163) had the best accuracy. In order to offer a reference for the accurate detection of soil physicochemical properties in tea plantations, this study investigated the modeling effect of each physicochemical property under various soil particle sizes and integrated the regression model with various downscaling strategies.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：山东省农业科学院文献资源数据库平台

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台