Improved Spectral Detection of Nitrogen Deficiency and Yellow Mosaic Disease Stresses in Wheat Using a Soil Effect Removal Algorithm and Machine Learning
Wheat yellow mosaic disease is a low-temperature and soil-borne disease. Crop infection by the yellow mosaic virus can lead to severe yield and economic losses. It is easily confused with nitrogen deficiency based on the plant’s morphological characteristics. Timely disease detection and crop manage...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/10/2513 |
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| author | Ziheng Feng Haiyan Zhang Jianzhao Duan Li He Xinru Yuan Yuezhi Gao Wandai Liu Xiao Li Wei Feng |
| author_facet | Ziheng Feng Haiyan Zhang Jianzhao Duan Li He Xinru Yuan Yuezhi Gao Wandai Liu Xiao Li Wei Feng |
| author_sort | Ziheng Feng |
| collection | DOAJ |
| description | Wheat yellow mosaic disease is a low-temperature and soil-borne disease. Crop infection by the yellow mosaic virus can lead to severe yield and economic losses. It is easily confused with nitrogen deficiency based on the plant’s morphological characteristics. Timely disease detection and crop management in the field require the precise identification of crop stress types. However, the detection of crop stress is often underappreciated. Wheat nitrogen deficiency and yellow mosaic disease were investigated in the field and wheat physiological and biochemical experiments were conducted to collect agronomic indicators, four years of reflectance spectral data at green-up and jointing were collected, and then studies for the detection of nitrogen deficiency and yellow mosaic disease stresses were carried out. The continuous removal (CR), first-order derivative (FD), standard normal variate (SNV), and spectral separation of soil and vegetation (3SV) preprocessing methods and 96 spectral indices were evaluated. The threshold method and variance inflation factor (TVIF) were used as feature selection methods combined with machine learning to develop a crop stress detection method. The results show that the most sensitive wavelengths are found in the 725–1000 nm region, while the sensitivity of the spectrum in the 400–725 nm region is lower. The PRI<sub>670,570</sub>, B, and RARSa spectral indices can detect nitrogen deficiency and yellow leaf disease stress, and the OA and Kappa values are 93.87% and 0.873, respectively, for PRI<sub>670,570</sub>, which is the best index. A 3SV-TVIF-SVM stress detection method was then proposed, using OA and Kappa values of 96.97% and 0.931, respectively, for field data validation. The results of the study can provide technical support and a theoretical basis for the accurate control of yellow mosaic disease and nitrogen fertilizer management in the field. |
| first_indexed | 2024-03-11T03:22:00Z |
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| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-03-11T03:22:00Z |
| publishDate | 2023-05-01 |
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| series | Remote Sensing |
| spelling | doaj.art-084fc3ca60e74df4aa5ff4a018f97beb2023-11-18T03:06:10ZengMDPI AGRemote Sensing2072-42922023-05-011510251310.3390/rs15102513Improved Spectral Detection of Nitrogen Deficiency and Yellow Mosaic Disease Stresses in Wheat Using a Soil Effect Removal Algorithm and Machine LearningZiheng Feng0Haiyan Zhang1Jianzhao Duan2Li He3Xinru Yuan4Yuezhi Gao5Wandai Liu6Xiao Li7Wei Feng8National Engineering Research Center for Wheat/State Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, Zhengzhou 450046, ChinaNational Engineering Research Center for Wheat/State Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, Zhengzhou 450046, ChinaNational Engineering Research Center for Wheat/State Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, Zhengzhou 450046, ChinaNational Engineering Research Center for Wheat/State Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, Zhengzhou 450046, ChinaNational Engineering Research Center for Wheat/State Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, Zhengzhou 450046, ChinaNational Engineering Research Center for Wheat/State Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, Zhengzhou 450046, ChinaNational Engineering Research Center for Wheat/State Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, Zhengzhou 450046, ChinaCollege of Science, Henan Agriculture University, Zhengzhou 450046, ChinaNational Engineering Research Center for Wheat/State Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, Zhengzhou 450046, ChinaWheat yellow mosaic disease is a low-temperature and soil-borne disease. Crop infection by the yellow mosaic virus can lead to severe yield and economic losses. It is easily confused with nitrogen deficiency based on the plant’s morphological characteristics. Timely disease detection and crop management in the field require the precise identification of crop stress types. However, the detection of crop stress is often underappreciated. Wheat nitrogen deficiency and yellow mosaic disease were investigated in the field and wheat physiological and biochemical experiments were conducted to collect agronomic indicators, four years of reflectance spectral data at green-up and jointing were collected, and then studies for the detection of nitrogen deficiency and yellow mosaic disease stresses were carried out. The continuous removal (CR), first-order derivative (FD), standard normal variate (SNV), and spectral separation of soil and vegetation (3SV) preprocessing methods and 96 spectral indices were evaluated. The threshold method and variance inflation factor (TVIF) were used as feature selection methods combined with machine learning to develop a crop stress detection method. The results show that the most sensitive wavelengths are found in the 725–1000 nm region, while the sensitivity of the spectrum in the 400–725 nm region is lower. The PRI<sub>670,570</sub>, B, and RARSa spectral indices can detect nitrogen deficiency and yellow leaf disease stress, and the OA and Kappa values are 93.87% and 0.873, respectively, for PRI<sub>670,570</sub>, which is the best index. A 3SV-TVIF-SVM stress detection method was then proposed, using OA and Kappa values of 96.97% and 0.931, respectively, for field data validation. The results of the study can provide technical support and a theoretical basis for the accurate control of yellow mosaic disease and nitrogen fertilizer management in the field.https://www.mdpi.com/2072-4292/15/10/2513nitrogen deficiencyyellow mosaic virusdetection of stressremote sensingmachine learning |
| spellingShingle | Ziheng Feng Haiyan Zhang Jianzhao Duan Li He Xinru Yuan Yuezhi Gao Wandai Liu Xiao Li Wei Feng Improved Spectral Detection of Nitrogen Deficiency and Yellow Mosaic Disease Stresses in Wheat Using a Soil Effect Removal Algorithm and Machine Learning Remote Sensing nitrogen deficiency yellow mosaic virus detection of stress remote sensing machine learning |
| title | Improved Spectral Detection of Nitrogen Deficiency and Yellow Mosaic Disease Stresses in Wheat Using a Soil Effect Removal Algorithm and Machine Learning |
| title_full | Improved Spectral Detection of Nitrogen Deficiency and Yellow Mosaic Disease Stresses in Wheat Using a Soil Effect Removal Algorithm and Machine Learning |
| title_fullStr | Improved Spectral Detection of Nitrogen Deficiency and Yellow Mosaic Disease Stresses in Wheat Using a Soil Effect Removal Algorithm and Machine Learning |
| title_full_unstemmed | Improved Spectral Detection of Nitrogen Deficiency and Yellow Mosaic Disease Stresses in Wheat Using a Soil Effect Removal Algorithm and Machine Learning |
| title_short | Improved Spectral Detection of Nitrogen Deficiency and Yellow Mosaic Disease Stresses in Wheat Using a Soil Effect Removal Algorithm and Machine Learning |
| title_sort | improved spectral detection of nitrogen deficiency and yellow mosaic disease stresses in wheat using a soil effect removal algorithm and machine learning |
| topic | nitrogen deficiency yellow mosaic virus detection of stress remote sensing machine learning |
| url | https://www.mdpi.com/2072-4292/15/10/2513 |
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