Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network–Random Forest–Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color Channels
Chlorophyll content is highly susceptible to environmental changes, and monitoring these changes can be a crucial tool for optimizing crop management and providing a foundation for research in plant physiology and ecology. This is expected to deepen our scientific understanding of plant ecological a...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/14/1/140 |
| _version_ | 1797344793551962112 |
|---|---|
| author | Jingming Wu Tiecheng Bai Xu Li |
| author_facet | Jingming Wu Tiecheng Bai Xu Li |
| author_sort | Jingming Wu |
| collection | DOAJ |
| description | Chlorophyll content is highly susceptible to environmental changes, and monitoring these changes can be a crucial tool for optimizing crop management and providing a foundation for research in plant physiology and ecology. This is expected to deepen our scientific understanding of plant ecological adaptation mechanisms, offer a basis for improving agricultural production, and contribute to ecosystem management. This study involved the collection of hyperspectral data, image data, and SPAD data from jujube leaves. These data were then processed using SG smoothing and the isolated forest algorithm, following which eigenvalues were extracted using a combination of Pearson’s phase relationship method and the Partial Least Squares Regression–continuous projection method. Subsequently, seven methods were employed to analyze the results, with hyperspectral data and color channel data used as independent variables in separate experiments. The findings indicated that the integrated BPNN-RF-Ridge Regression algorithm provided the best results, with an R<sup>2</sup> of 0.8249, MAE of 2.437, and RMSE of 2.9724. The inclusion of color channel data as an independent variable led to a 3.2% improvement in R<sup>2</sup>, with MAE and RMSE increasing by 1.6% and 3.9%, respectively. These results demonstrate the effectiveness of integrated methods for the determination of chlorophyll content in jujube leaves and underscore the potential of using multi-source data to improve the model fit with a minimal impact on errors. Further research is warranted to explore the application of these findings in precision agriculture for jujube yield optimization and income-related endeavors, as well as to provide insights for similar studies in other plant species. |
| first_indexed | 2024-03-08T11:08:04Z |
| format | Article |
| id | doaj.art-0764fcf7df794f63b02ac18fdf4eb83c |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-08T11:08:04Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-0764fcf7df794f63b02ac18fdf4eb83c2024-01-26T14:25:52ZengMDPI AGAgronomy2073-43952024-01-0114114010.3390/agronomy14010140Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network–Random Forest–Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color ChannelsJingming Wu0Tiecheng Bai1Xu Li2College of Information Engineering, Tarim University, Alaer 843300, ChinaCollege of Information Engineering, Tarim University, Alaer 843300, ChinaCollege of Information Engineering, Tarim University, Alaer 843300, ChinaChlorophyll content is highly susceptible to environmental changes, and monitoring these changes can be a crucial tool for optimizing crop management and providing a foundation for research in plant physiology and ecology. This is expected to deepen our scientific understanding of plant ecological adaptation mechanisms, offer a basis for improving agricultural production, and contribute to ecosystem management. This study involved the collection of hyperspectral data, image data, and SPAD data from jujube leaves. These data were then processed using SG smoothing and the isolated forest algorithm, following which eigenvalues were extracted using a combination of Pearson’s phase relationship method and the Partial Least Squares Regression–continuous projection method. Subsequently, seven methods were employed to analyze the results, with hyperspectral data and color channel data used as independent variables in separate experiments. The findings indicated that the integrated BPNN-RF-Ridge Regression algorithm provided the best results, with an R<sup>2</sup> of 0.8249, MAE of 2.437, and RMSE of 2.9724. The inclusion of color channel data as an independent variable led to a 3.2% improvement in R<sup>2</sup>, with MAE and RMSE increasing by 1.6% and 3.9%, respectively. These results demonstrate the effectiveness of integrated methods for the determination of chlorophyll content in jujube leaves and underscore the potential of using multi-source data to improve the model fit with a minimal impact on errors. Further research is warranted to explore the application of these findings in precision agriculture for jujube yield optimization and income-related endeavors, as well as to provide insights for similar studies in other plant species.https://www.mdpi.com/2073-4395/14/1/140jujube leafsoil and plant analyzer development (SPAD)hyperspectral datacolor channelBPNN-RF-Ridge Regressionnon-destructive testing |
| spellingShingle | Jingming Wu Tiecheng Bai Xu Li Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network–Random Forest–Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color Channels Agronomy jujube leaf soil and plant analyzer development (SPAD) hyperspectral data color channel BPNN-RF-Ridge Regression non-destructive testing |
| title | Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network–Random Forest–Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color Channels |
| title_full | Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network–Random Forest–Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color Channels |
| title_fullStr | Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network–Random Forest–Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color Channels |
| title_full_unstemmed | Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network–Random Forest–Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color Channels |
| title_short | Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network–Random Forest–Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color Channels |
| title_sort | inverting chlorophyll content in jujube leaves using a back propagation neural network random forest ridge regression algorithm with combined hyperspectral data and image color channels |
| topic | jujube leaf soil and plant analyzer development (SPAD) hyperspectral data color channel BPNN-RF-Ridge Regression non-destructive testing |
| url | https://www.mdpi.com/2073-4395/14/1/140 |
| work_keys_str_mv | AT jingmingwu invertingchlorophyllcontentinjujubeleavesusingabackpropagationneuralnetworkrandomforestridgeregressionalgorithmwithcombinedhyperspectraldataandimagecolorchannels AT tiechengbai invertingchlorophyllcontentinjujubeleavesusingabackpropagationneuralnetworkrandomforestridgeregressionalgorithmwithcombinedhyperspectraldataandimagecolorchannels AT xuli invertingchlorophyllcontentinjujubeleavesusingabackpropagationneuralnetworkrandomforestridgeregressionalgorithmwithcombinedhyperspectraldataandimagecolorchannels |