Estimation of Rice Protein Content Based on Unmanned Aerial Vehicle Hyperspectral Imaging
Identification of nutritious rice varieties through non-destructive detection technology is important for high-quality seed production. With the development of technology, rapid and non-destructive identification methods based on unmanned aerial vehicle (UAV) remote sensing technology are increasing...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/14/11/2479 |
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| author | Lei Yan Cen Liu Muhammad Zain Minghan Cheng Zhonhyang Huo Chenming Sun |
| author_facet | Lei Yan Cen Liu Muhammad Zain Minghan Cheng Zhonhyang Huo Chenming Sun |
| author_sort | Lei Yan |
| collection | DOAJ |
| description | Identification of nutritious rice varieties through non-destructive detection technology is important for high-quality seed production. With the development of technology, rapid and non-destructive identification methods based on unmanned aerial vehicle (UAV) remote sensing technology are increasingly gaining attention in the scientific community. This study utilized hyperspectral imaging technology to acquire spectral reflectance data from the rice canopy during the grain filling stage. Different models (stepwise multiple linear regression (SMLR) and the Back Propagation Neural Network (BPNN)) for estimating rice protein content based on canopy spectral information were constructed using both multiple stepwise regression and BP neural networks. The results showed that the model based on BPNN estimation performed best for predicting grain protein content, with an R<sup>2</sup> = 0.9516 and RMSE = 0.3492, indicating high accuracy and stability in the model. Overall, hyperspectral imaging technology combined with various models could significantly help to identify rice varieties. Further, the current findings provide a technical reference for the selection of high-quality rice varieties in a non-destructive manner. |
| format | Article |
| id | doaj-art-16157f0c990e43c3bd48b98fba4dc0b8 |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-16157f0c990e43c3bd48b98fba4dc0b82025-08-20T02:26:51ZengMDPI AGAgronomy2073-43952024-10-011411247910.3390/agronomy14112479Estimation of Rice Protein Content Based on Unmanned Aerial Vehicle Hyperspectral ImagingLei Yan0Cen Liu1Muhammad Zain2Minghan Cheng3Zhonhyang Huo4Chenming Sun5Cultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaIdentification of nutritious rice varieties through non-destructive detection technology is important for high-quality seed production. With the development of technology, rapid and non-destructive identification methods based on unmanned aerial vehicle (UAV) remote sensing technology are increasingly gaining attention in the scientific community. This study utilized hyperspectral imaging technology to acquire spectral reflectance data from the rice canopy during the grain filling stage. Different models (stepwise multiple linear regression (SMLR) and the Back Propagation Neural Network (BPNN)) for estimating rice protein content based on canopy spectral information were constructed using both multiple stepwise regression and BP neural networks. The results showed that the model based on BPNN estimation performed best for predicting grain protein content, with an R<sup>2</sup> = 0.9516 and RMSE = 0.3492, indicating high accuracy and stability in the model. Overall, hyperspectral imaging technology combined with various models could significantly help to identify rice varieties. Further, the current findings provide a technical reference for the selection of high-quality rice varieties in a non-destructive manner.https://www.mdpi.com/2073-4395/14/11/2479riceprotein contenthyperspectralunmanned aerial vehicle (UAV)estimation model |
| spellingShingle | Lei Yan Cen Liu Muhammad Zain Minghan Cheng Zhonhyang Huo Chenming Sun Estimation of Rice Protein Content Based on Unmanned Aerial Vehicle Hyperspectral Imaging Agronomy rice protein content hyperspectral unmanned aerial vehicle (UAV) estimation model |
| title | Estimation of Rice Protein Content Based on Unmanned Aerial Vehicle Hyperspectral Imaging |
| title_full | Estimation of Rice Protein Content Based on Unmanned Aerial Vehicle Hyperspectral Imaging |
| title_fullStr | Estimation of Rice Protein Content Based on Unmanned Aerial Vehicle Hyperspectral Imaging |
| title_full_unstemmed | Estimation of Rice Protein Content Based on Unmanned Aerial Vehicle Hyperspectral Imaging |
| title_short | Estimation of Rice Protein Content Based on Unmanned Aerial Vehicle Hyperspectral Imaging |
| title_sort | estimation of rice protein content based on unmanned aerial vehicle hyperspectral imaging |
| topic | rice protein content hyperspectral unmanned aerial vehicle (UAV) estimation model |
| url | https://www.mdpi.com/2073-4395/14/11/2479 |
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