土壤有机质预测模型数据

可以用于土壤有机质预测,输入为土壤样本的光谱901nm吸光度，905nm吸光度，909nm吸光度，913nm吸光度，输出为土壤有机质预测。该模型帮助解决了土壤有机质和土壤光谱吸光度关系建模的问题。使用光谱仪器采集土壤光谱数据，将采集的土壤光谱数据使用传统算法，多元线性回归算法等方式以预测土壤有机质。该模型通过输入土壤光谱901nm吸光度、905nm吸光度、909nm吸光度、913nm吸光度数据，来输出预测的土壤有机质。

This dataset is applicable for soil organic matter prediction. The input features are the absorbance values of soil samples at 901nm, 905nm, 909nm, and 913nm spectral wavelengths, and the output is the predicted soil organic matter content. This model addresses the challenge of modeling the relationship between soil organic matter and soil spectral absorbance. Soil spectral data is collected using a spectrophotometer, and conventional algorithms such as multiple linear regression were previously employed to predict soil organic matter from the collected spectral data. This model takes the absorbance values at 901nm, 905nm, 909nm, and 913nm as inputs to generate the predicted soil organic matter content.

搜集汇总

数据集介绍

特点

该数据集包含270条土壤有机质预测数据，输入为901nm、905nm、909nm和913nm的光谱吸光度，输出为预测的有机质含量。数据每年更新，适用于农业领域的土壤有机质预测，采用多元线性回归算法进行建模。