Estimation of Nitrogen Concentration in Walnut Canopies in Southern Xinjiang Based on UAV Multispectral Images
Nitrogen is one of the most important nutrients affecting the growth and fruit quality of walnut trees. Rapid and accurate monitoring of nitrogen concentration in the walnut canopy can provide a digital basis for its nutritional diagnosis and precision fertilization. Consequently, the main purpose o...
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MDPI AG
2023-06-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/6/1604 |
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| author | Yu Wang Chunhui Feng Yiru Ma Xiangyu Chen Bin Lu Yan Song Ze Zhang Rui Zhang |
| author_facet | Yu Wang Chunhui Feng Yiru Ma Xiangyu Chen Bin Lu Yan Song Ze Zhang Rui Zhang |
| author_sort | Yu Wang |
| collection | DOAJ |
| description | Nitrogen is one of the most important nutrients affecting the growth and fruit quality of walnut trees. Rapid and accurate monitoring of nitrogen concentration in the walnut canopy can provide a digital basis for its nutritional diagnosis and precision fertilization. Consequently, the main purpose of this study was to use Unmanned Aerial Vehicle (UAV) remote sensing technology to monitor the nitrogen concentration of walnut canopies. In this study, UAV multispectral images of the canopies of nine walnut orchards with different management levels in Wensu County, South Xinjiang, China, were collected during the fast-growing (20 May), sclerotization (25 June), and near-maturity (27 August) periods of walnut fruit, and canopy nitrogen concentration data for 180 individual plants were collected during the same periods. The validity of the information extracted via the outline canopy and simulated canopy methods was compared. The accuracy of nitrogen concentration inversion for three modeling methods, partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF), was analyzed; the effects of different combinations of variables on model accuracy were compared; and the spatial distribution of the nitrogen concentration in the walnut canopy was numerically mapped using the optimal model. The results showed that the accuracy of the model created using the single plant information extracted from the outlined canopy was better than that of the simulated canopy method, but the simulated canopy method was more efficient in extracting effective information from the single plant canopy than the outlined canopy. The simulated canopy method overcame the difficulty of mismatching the spectral information of individual plants extracted, by outlining the canopy in the original image for nitrogen distribution mapping with the spectral information of image elements in the original resolution image. The prediction accuracy of the RF model was better than that of the SVM and PLSR models; the prediction accuracy of the model using a combination of waveband texture information and vegetation index texture information was better than that of the single-source model. The coefficients of determination (R<sup>2</sup>) values of the RF prediction model built using the band texture information extracted via the simulated canopy method with the vegetation index texture information were in the range of 0.61–0.84, the root mean square error (RMSE) values were in the range of 0.27–0.43 g kg<sup>−1</sup>, and the relative analysis error (RPD) values were in the range of 1.58–2.20. This study shows that it is feasible to monitor the nitrogen concentration of walnut tree canopies using UAV multispectral remote sensing. This study provides a theoretical basis and methodological reference for the rapid monitoring of nutrients in fruit trees in southern Xinjiang. |
| first_indexed | 2024-03-11T02:51:40Z |
| format | Article |
| id | doaj.art-500d00c4ba7c402b87a0a712eddf556f |
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| last_indexed | 2024-03-11T02:51:40Z |
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| spelling | doaj.art-500d00c4ba7c402b87a0a712eddf556f2023-11-18T08:55:31ZengMDPI AGAgronomy2073-43952023-06-01136160410.3390/agronomy13061604Estimation of Nitrogen Concentration in Walnut Canopies in Southern Xinjiang Based on UAV Multispectral ImagesYu Wang0Chunhui Feng1Yiru Ma2Xiangyu Chen3Bin Lu4Yan Song5Ze Zhang6Rui Zhang7College of Life Sciences and Technology, Tarim University, Alar 843300, ChinaCollege of Horticulture and Forestry, Tarim University, Alar 843300, ChinaXinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, Agricultural College of Shihezi University, Shihezi 832003, ChinaXinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, Agricultural College of Shihezi University, Shihezi 832003, ChinaYunnan Academy of Forestry and Grassland, Kunming 650000, ChinaHorticulture College, Xinjiang Agricultural University, Urumqi 830000, ChinaXinjiang Production and Construction Crops Oasis Eco-Agriculture Key Laboratory, Agricultural College of Shihezi University, Shihezi 832003, ChinaState Key Laboratory Breeding Base for the Protection and Utilization of Biological Resources in Tarim Basin Co-Funded by Xinjiang Corps and the Ministry of Science and Technology, Tarim University, Alar 843300, ChinaNitrogen is one of the most important nutrients affecting the growth and fruit quality of walnut trees. Rapid and accurate monitoring of nitrogen concentration in the walnut canopy can provide a digital basis for its nutritional diagnosis and precision fertilization. Consequently, the main purpose of this study was to use Unmanned Aerial Vehicle (UAV) remote sensing technology to monitor the nitrogen concentration of walnut canopies. In this study, UAV multispectral images of the canopies of nine walnut orchards with different management levels in Wensu County, South Xinjiang, China, were collected during the fast-growing (20 May), sclerotization (25 June), and near-maturity (27 August) periods of walnut fruit, and canopy nitrogen concentration data for 180 individual plants were collected during the same periods. The validity of the information extracted via the outline canopy and simulated canopy methods was compared. The accuracy of nitrogen concentration inversion for three modeling methods, partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF), was analyzed; the effects of different combinations of variables on model accuracy were compared; and the spatial distribution of the nitrogen concentration in the walnut canopy was numerically mapped using the optimal model. The results showed that the accuracy of the model created using the single plant information extracted from the outlined canopy was better than that of the simulated canopy method, but the simulated canopy method was more efficient in extracting effective information from the single plant canopy than the outlined canopy. The simulated canopy method overcame the difficulty of mismatching the spectral information of individual plants extracted, by outlining the canopy in the original image for nitrogen distribution mapping with the spectral information of image elements in the original resolution image. The prediction accuracy of the RF model was better than that of the SVM and PLSR models; the prediction accuracy of the model using a combination of waveband texture information and vegetation index texture information was better than that of the single-source model. The coefficients of determination (R<sup>2</sup>) values of the RF prediction model built using the band texture information extracted via the simulated canopy method with the vegetation index texture information were in the range of 0.61–0.84, the root mean square error (RMSE) values were in the range of 0.27–0.43 g kg<sup>−1</sup>, and the relative analysis error (RPD) values were in the range of 1.58–2.20. This study shows that it is feasible to monitor the nitrogen concentration of walnut tree canopies using UAV multispectral remote sensing. This study provides a theoretical basis and methodological reference for the rapid monitoring of nutrients in fruit trees in southern Xinjiang.https://www.mdpi.com/2073-4395/13/6/1604walnutunmanned aerial vehiclenitrogen concentrationmultispectralsimulated canopy method |
| spellingShingle | Yu Wang Chunhui Feng Yiru Ma Xiangyu Chen Bin Lu Yan Song Ze Zhang Rui Zhang Estimation of Nitrogen Concentration in Walnut Canopies in Southern Xinjiang Based on UAV Multispectral Images Agronomy walnut unmanned aerial vehicle nitrogen concentration multispectral simulated canopy method |
| title | Estimation of Nitrogen Concentration in Walnut Canopies in Southern Xinjiang Based on UAV Multispectral Images |
| title_full | Estimation of Nitrogen Concentration in Walnut Canopies in Southern Xinjiang Based on UAV Multispectral Images |
| title_fullStr | Estimation of Nitrogen Concentration in Walnut Canopies in Southern Xinjiang Based on UAV Multispectral Images |
| title_full_unstemmed | Estimation of Nitrogen Concentration in Walnut Canopies in Southern Xinjiang Based on UAV Multispectral Images |
| title_short | Estimation of Nitrogen Concentration in Walnut Canopies in Southern Xinjiang Based on UAV Multispectral Images |
| title_sort | estimation of nitrogen concentration in walnut canopies in southern xinjiang based on uav multispectral images |
| topic | walnut unmanned aerial vehicle nitrogen concentration multispectral simulated canopy method |
| url | https://www.mdpi.com/2073-4395/13/6/1604 |
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