Hyperspectral imaging predicts yield and nitrogen content in grass-legume polyculturesem

predict_nutrient.py demonstrates PLSR modelling using Bootstrap validation and it was tested in Python 3.6. The folder "organised_data" includes all of the pre-processed data, including reflectance data and laboratory-measured data. The program conducts the following parts: 1. Trains a PLSR model using the original data and then validates the model using the original data. The validation results will be saved in a .xlsx file with column names of 'xxx_full'. 2. Trains a PLSR model using the Bootstrap data (re-sampling with replacement) and then validates the model using the original data. The validation results will be saved in the .xlsx file with column names of 'xxx_bs. 3. Trains a PLSR model using the Bootstrap data and then validates the model using the Bootstrap data. The validation results will be saved in the .xlsx file with column names 'xxx_a'.predict_nutrient.py 演示了采用自助法（Bootstrap）验证的偏最小二乘回归（Partial Least Squares Regression, PLSR）建模方案，该代码已在Python 3.6环境中完成测试。文件夹"organised_data"内包含全部预处理后的数据，涵盖反射光谱数据与实验室实测数据。该程序将执行以下三项任务： 1. 基于原始数据集训练偏最小二乘回归模型，随后使用原始数据集完成模型验证，验证结果将保存至.xlsx格式文件，列名格式为"xxx_full"。 2. 基于经有放回重采样得到的自助采样数据集训练偏最小二乘回归模型，随后使用原始数据集完成模型验证，验证结果将保存至.xlsx格式文件，列名格式为"xxx_bs"。 3. 基于自助采样数据集训练偏最小二乘回归模型，随后使用该自助采样数据集完成模型验证，验证结果将保存至.xlsx格式文件，列名格式为"xxx_a"。