Multispectral Inversion of Starch Content in Rice Grains from Yingjiang County Based on Feature Band Selection Algorithms
The starch content in rice grains is a key factor in determining their quality. An optimal starch level not only ensures grain fullness, improving storage stability, but also enhances the stickiness and viscosity of cooked rice, thereby boosting its palatability and nutritional value. However, tradi...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/1/86 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589385601646592 |
|---|---|
| author | Xiaotong Su Zhifang Zhao Min Zeng Fei Zhao Ziyang Li Yu Zheng |
| author_facet | Xiaotong Su Zhifang Zhao Min Zeng Fei Zhao Ziyang Li Yu Zheng |
| author_sort | Xiaotong Su |
| collection | DOAJ |
| description | The starch content in rice grains is a key factor in determining their quality. An optimal starch level not only ensures grain fullness, improving storage stability, but also enhances the stickiness and viscosity of cooked rice, thereby boosting its palatability and nutritional value. However, traditional methods for monitoring starch content are expensive and lack the capability to provide rapid spatial distribution information across large areas. To address this limitation, this study focuses on mature rice grains in the Yingjiang region, leveraging multispectral data from the Sentinel-2 satellite. First and second derivative transformations were applied to the multispectral reflectance data, followed by the use of three feature selection algorithms to identify key spectral bands. BP neural networks and ELM neural network regression models were then integrated to quantitatively estimate starch content across the study area. As a result, high-precision spatial distribution maps of starch content were generated, providing a novel and efficient method for large-scale rapid monitoring. The results demonstrate that, compared to full-band data, the use of SPA feature selection significantly improved the predictive accuracy of both BP and ELM models, despite a slight increase in the models’ MSE. Similarly, CARS feature selection also contributed substantially to enhancing the accuracy of the BP and ELM models. In contrast, UVE feature selection significantly reduced the MSE of the BP model, improving predictive precision, with the model achieving an <i>R</i><sup>2</sup> of 0.8061 and an MSE of 0.3896. This study highlights that the inversion method, which combines feature selection algorithms with machine learning models, can effectively enhance the predictive accuracy of starch content estimation. Among the tested approaches, the combination of UVE feature selection and BP neural networks delivered the best performance. These findings confirm the feasibility of utilizing Sentinel-2 satellite multispectral data for the quantitative inversion of agronomic parameters across large agricultural areas, providing robust technical support for precision agriculture. |
| format | Article |
| id | doaj-art-2a77dd6d7e8847a2a69f91de00e8dc30 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-2a77dd6d7e8847a2a69f91de00e8dc302025-01-24T13:16:40ZengMDPI AGAgronomy2073-43952024-12-011518610.3390/agronomy15010086Multispectral Inversion of Starch Content in Rice Grains from Yingjiang County Based on Feature Band Selection AlgorithmsXiaotong Su0Zhifang Zhao1Min Zeng2Fei Zhao3Ziyang Li4Yu Zheng5Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, ChinaSchool of Earth Sciences, Yunnan University, Kunming 650500, ChinaSchool of Earth Sciences, Yunnan University, Kunming 650500, ChinaSchool of Earth Sciences, Yunnan University, Kunming 650500, ChinaYunnan Institute of Geological Sciences, Kunming 650011, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, ChinaThe starch content in rice grains is a key factor in determining their quality. An optimal starch level not only ensures grain fullness, improving storage stability, but also enhances the stickiness and viscosity of cooked rice, thereby boosting its palatability and nutritional value. However, traditional methods for monitoring starch content are expensive and lack the capability to provide rapid spatial distribution information across large areas. To address this limitation, this study focuses on mature rice grains in the Yingjiang region, leveraging multispectral data from the Sentinel-2 satellite. First and second derivative transformations were applied to the multispectral reflectance data, followed by the use of three feature selection algorithms to identify key spectral bands. BP neural networks and ELM neural network regression models were then integrated to quantitatively estimate starch content across the study area. As a result, high-precision spatial distribution maps of starch content were generated, providing a novel and efficient method for large-scale rapid monitoring. The results demonstrate that, compared to full-band data, the use of SPA feature selection significantly improved the predictive accuracy of both BP and ELM models, despite a slight increase in the models’ MSE. Similarly, CARS feature selection also contributed substantially to enhancing the accuracy of the BP and ELM models. In contrast, UVE feature selection significantly reduced the MSE of the BP model, improving predictive precision, with the model achieving an <i>R</i><sup>2</sup> of 0.8061 and an MSE of 0.3896. This study highlights that the inversion method, which combines feature selection algorithms with machine learning models, can effectively enhance the predictive accuracy of starch content estimation. Among the tested approaches, the combination of UVE feature selection and BP neural networks delivered the best performance. These findings confirm the feasibility of utilizing Sentinel-2 satellite multispectral data for the quantitative inversion of agronomic parameters across large agricultural areas, providing robust technical support for precision agriculture.https://www.mdpi.com/2073-4395/15/1/86precision agriculturerice grainsfeature selectionSentinel-2 multispectral datamachine learning |
| spellingShingle | Xiaotong Su Zhifang Zhao Min Zeng Fei Zhao Ziyang Li Yu Zheng Multispectral Inversion of Starch Content in Rice Grains from Yingjiang County Based on Feature Band Selection Algorithms Agronomy precision agriculture rice grains feature selection Sentinel-2 multispectral data machine learning |
| title | Multispectral Inversion of Starch Content in Rice Grains from Yingjiang County Based on Feature Band Selection Algorithms |
| title_full | Multispectral Inversion of Starch Content in Rice Grains from Yingjiang County Based on Feature Band Selection Algorithms |
| title_fullStr | Multispectral Inversion of Starch Content in Rice Grains from Yingjiang County Based on Feature Band Selection Algorithms |
| title_full_unstemmed | Multispectral Inversion of Starch Content in Rice Grains from Yingjiang County Based on Feature Band Selection Algorithms |
| title_short | Multispectral Inversion of Starch Content in Rice Grains from Yingjiang County Based on Feature Band Selection Algorithms |
| title_sort | multispectral inversion of starch content in rice grains from yingjiang county based on feature band selection algorithms |
| topic | precision agriculture rice grains feature selection Sentinel-2 multispectral data machine learning |
| url | https://www.mdpi.com/2073-4395/15/1/86 |
| work_keys_str_mv | AT xiaotongsu multispectralinversionofstarchcontentinricegrainsfromyingjiangcountybasedonfeaturebandselectionalgorithms AT zhifangzhao multispectralinversionofstarchcontentinricegrainsfromyingjiangcountybasedonfeaturebandselectionalgorithms AT minzeng multispectralinversionofstarchcontentinricegrainsfromyingjiangcountybasedonfeaturebandselectionalgorithms AT feizhao multispectralinversionofstarchcontentinricegrainsfromyingjiangcountybasedonfeaturebandselectionalgorithms AT ziyangli multispectralinversionofstarchcontentinricegrainsfromyingjiangcountybasedonfeaturebandselectionalgorithms AT yuzheng multispectralinversionofstarchcontentinricegrainsfromyingjiangcountybasedonfeaturebandselectionalgorithms |