Efficient determination of water content in potato leaves based on spectroscopy technology

Table 1.
Descriptive statistics of moisture content in the potato leaves
马铃薯叶片水分含量统计
View table
View in Article
Table 1.
Descriptive statistics of moisture content in the potato leaves
马铃薯叶片水分含量统计
数据集样本数最大值/% 最小值/% 平均值/% 平均偏差/%
总样本 110 92.35 80.00 86.82 2.579
建模集 75 92.35 80.00 86.92 2.575
预测集 35 91.33 80.09 86.59 2.573

Table 2.
All-band data PLSR model prediction results with different methods
不同方法的全波段数据PLSR模型预测结果

View table

View in Article

Table 2.
All-band data PLSR model prediction results with different methods
不同方法的全波段数据PLSR模型预测结果

处理方法	建模集		验证集		预测集
处理方法	R_c²	RMSEC	R_cv²	RMSECV	R_p²	RMSEP
无预处理	0.8907	0.8513	0.8658	0.6911	0.8493	0.9989
SG平滑	0.8907	0.8515	0.8656	0.6916	0.8493	0.9988
MSC	0.8906	0.8513	0.8657	0.6912	0.8492	0.9988
SNV	0.9068	0.7862	0.8725	0.6735	0.8363	1.0412

Table 3.
All-band data BP neural network mode prediction results with different methods
不同方法的全波段数据BP神经网络模型预测结果
View table
View in Article
Table 3.
All-band data BP neural network mode prediction results with different methods
不同方法的全波段数据BP神经网络模型预测结果
处理方法预测集
R_p² RMSEP
无预处理 0.9655 0.4776
SG平滑 0.9747 0.4095
MSC 0.9791 0.3723
SNV 0.8519 0.9902

Table 4.
Prediction results of different methods feature band PLSR model
不同方法特征波段数据PLSR模型预测结果

View table

View in Article

Table 4.
Prediction results of different methods feature band PLSR model
不同方法特征波段数据PLSR模型预测结果

处理方法	建模集		验证集		预测集
处理方法	R_c²	RMSEC	R_cv²	RMSECV	R_p²	RMSEP
无预处理	0.8783	0.8982	0.8407	0.7529	0.8468	1.0070
SG平滑	0.8772	0.9024	0.8528	0.7238	0.8492	0.9991
MSC	0.8212	1.1788	0.8036	0.8575	0.7745	0.9899
SNV	0.8164	1.1035	0.7624	0.9196	0.7520	1.2813

Table 5.
Prediction results of BP neural network models with different methods
不同方法特征波段数据BP神经网络模型预测结果
View table
View in Article
Table 5.
Prediction results of BP neural network models with different methods
不同方法特征波段数据BP神经网络模型预测结果
处理方法预测集
R_p² RMSEP
无预处理 0.9170 0.7411
SG平滑 0.9658 0.4759
MSC 0.8351 1.0498
SNV 0.7660 1.2446

Tools

Get Citation

Copy Citation Text

Xufeng YU, Hongmei LI, Wei ZHUO, Jie FENG. Efficient determination of water content in potato leaves based on spectroscopy technology[J]. Optical Instruments, 2020, 42(4): 7

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: APPLICATION TECHNOLOGY

Received: Mar. 30, 2020

Accepted: --

Published Online: Jan. 6, 2021

The Author Email: FENG Jie (fengjie_yunnan@126.com)

DOI:10.3969/j.issn.1005-5630.2020.04.002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Descriptive statistics of moisture content in the potato leaves马铃薯叶片水分含量统计

Table 1. Descriptive statistics of moisture content in the potato leaves马铃薯叶片水分含量统计

Table 2. All-band data PLSR model prediction results with different methods不同方法的全波段数据PLSR模型预测结果

Table 2. All-band data PLSR model prediction results with different methods不同方法的全波段数据PLSR模型预测结果

Table 3. All-band data BP neural network mode prediction results with different methods不同方法的全波段数据BP神经网络模型预测结果

Table 3. All-band data BP neural network mode prediction results with different methods不同方法的全波段数据BP神经网络模型预测结果

Table 4. Prediction results of different methods feature band PLSR model不同方法特征波段数据PLSR模型预测结果

Table 4. Prediction results of different methods feature band PLSR model不同方法特征波段数据PLSR模型预测结果

Table 5. Prediction results of BP neural network models with different methods不同方法特征波段数据BP神经网络模型预测结果

Table 5. Prediction results of BP neural network models with different methods不同方法特征波段数据BP神经网络模型预测结果

微信扫一扫：分享

Table 1.
Descriptive statistics of moisture content in the potato leaves
马铃薯叶片水分含量统计

Table 1.
Descriptive statistics of moisture content in the potato leaves
马铃薯叶片水分含量统计

Table 2.
All-band data PLSR model prediction results with different methods
不同方法的全波段数据PLSR模型预测结果

Table 2.
All-band data PLSR model prediction results with different methods
不同方法的全波段数据PLSR模型预测结果

Table 3.
All-band data BP neural network mode prediction results with different methods
不同方法的全波段数据BP神经网络模型预测结果

Table 3.
All-band data BP neural network mode prediction results with different methods
不同方法的全波段数据BP神经网络模型预测结果

Table 4.
Prediction results of different methods feature band PLSR model
不同方法特征波段数据PLSR模型预测结果

Table 4.
Prediction results of different methods feature band PLSR model
不同方法特征波段数据PLSR模型预测结果

Table 5.
Prediction results of BP neural network models with different methods
不同方法特征波段数据BP神经网络模型预测结果

Table 5.
Prediction results of BP neural network models with different methods
不同方法特征波段数据BP神经网络模型预测结果