Predicting Wheat Leaf Nitrogen Content by Combining Deep Multitask Learning and a Mechanistic Model Using UAV Hyperspectral Images
Predicting leaf nitrogen content (LNC) using unmanned aerial vehicle (UAV) images is of great significance. Traditional LNC prediction methods based on empirical and mechanistic models have limitations. This study aimed to propose a new LNC prediction method based on combining deep learning methods...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/24/6334 |
| _version_ | 1797455467330404352 |
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| author | Xiao Ma Pengfei Chen Xiuliang Jin |
| author_facet | Xiao Ma Pengfei Chen Xiuliang Jin |
| author_sort | Xiao Ma |
| collection | DOAJ |
| description | Predicting leaf nitrogen content (LNC) using unmanned aerial vehicle (UAV) images is of great significance. Traditional LNC prediction methods based on empirical and mechanistic models have limitations. This study aimed to propose a new LNC prediction method based on combining deep learning methods and mechanistic models. Wheat field experiments were conducted to make plants with different LNC values. The LNC and UAV hyperspectral images were collected during the critical growth stages of wheat. Based on these data, a method combining the deep multitask learning method and the N-based PROSAIL model was proposed and compared with traditional LNC prediction methods, including spectral index (SI), partial least squares regression (PLSR) and artificial neural network (ANN) methods. The results show that the new proposed method obtained the best LNC prediction results, with <i>R</i><sup>2</sup>, <i>RMSE</i> and <i>RMSE</i>% values of 0.79, 20.86 μg/cm<sup>2</sup> and 18.63%, respectively, during calibration and 0.82, 18.40 μg/cm<sup>2</sup> and 16.92%, respectively, during validation. The other methods obtained <i>R</i><sup>2</sup>, <i>RMSE</i> and <i>RMSE</i>% values between 0.29 and 0.68, 25.71 and 38.52 μg/cm<sup>2</sup> and 22.95 and 34.39%, respectively, during calibration and between 0.43 and 0.74, 22.79 and 33.55 μg/cm<sup>2</sup> and 20.96 and 30.86%, respectively, during validation. Thus, this study provides an accurate LNC prediction tool for precise nitrogen (N) management in the field. |
| first_indexed | 2024-03-09T15:53:51Z |
| format | Article |
| id | doaj.art-b9a3b31d5b1b4512904c1f1922436a84 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T15:53:51Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b9a3b31d5b1b4512904c1f1922436a842023-11-24T17:48:01ZengMDPI AGRemote Sensing2072-42922022-12-011424633410.3390/rs14246334Predicting Wheat Leaf Nitrogen Content by Combining Deep Multitask Learning and a Mechanistic Model Using UAV Hyperspectral ImagesXiao Ma0Pengfei Chen1Xiuliang Jin2State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaPredicting leaf nitrogen content (LNC) using unmanned aerial vehicle (UAV) images is of great significance. Traditional LNC prediction methods based on empirical and mechanistic models have limitations. This study aimed to propose a new LNC prediction method based on combining deep learning methods and mechanistic models. Wheat field experiments were conducted to make plants with different LNC values. The LNC and UAV hyperspectral images were collected during the critical growth stages of wheat. Based on these data, a method combining the deep multitask learning method and the N-based PROSAIL model was proposed and compared with traditional LNC prediction methods, including spectral index (SI), partial least squares regression (PLSR) and artificial neural network (ANN) methods. The results show that the new proposed method obtained the best LNC prediction results, with <i>R</i><sup>2</sup>, <i>RMSE</i> and <i>RMSE</i>% values of 0.79, 20.86 μg/cm<sup>2</sup> and 18.63%, respectively, during calibration and 0.82, 18.40 μg/cm<sup>2</sup> and 16.92%, respectively, during validation. The other methods obtained <i>R</i><sup>2</sup>, <i>RMSE</i> and <i>RMSE</i>% values between 0.29 and 0.68, 25.71 and 38.52 μg/cm<sup>2</sup> and 22.95 and 34.39%, respectively, during calibration and between 0.43 and 0.74, 22.79 and 33.55 μg/cm<sup>2</sup> and 20.96 and 30.86%, respectively, during validation. Thus, this study provides an accurate LNC prediction tool for precise nitrogen (N) management in the field.https://www.mdpi.com/2072-4292/14/24/6334leaf nitrogen contenthybrid methodUAVhyperspectral image |
| spellingShingle | Xiao Ma Pengfei Chen Xiuliang Jin Predicting Wheat Leaf Nitrogen Content by Combining Deep Multitask Learning and a Mechanistic Model Using UAV Hyperspectral Images Remote Sensing leaf nitrogen content hybrid method UAV hyperspectral image |
| title | Predicting Wheat Leaf Nitrogen Content by Combining Deep Multitask Learning and a Mechanistic Model Using UAV Hyperspectral Images |
| title_full | Predicting Wheat Leaf Nitrogen Content by Combining Deep Multitask Learning and a Mechanistic Model Using UAV Hyperspectral Images |
| title_fullStr | Predicting Wheat Leaf Nitrogen Content by Combining Deep Multitask Learning and a Mechanistic Model Using UAV Hyperspectral Images |
| title_full_unstemmed | Predicting Wheat Leaf Nitrogen Content by Combining Deep Multitask Learning and a Mechanistic Model Using UAV Hyperspectral Images |
| title_short | Predicting Wheat Leaf Nitrogen Content by Combining Deep Multitask Learning and a Mechanistic Model Using UAV Hyperspectral Images |
| title_sort | predicting wheat leaf nitrogen content by combining deep multitask learning and a mechanistic model using uav hyperspectral images |
| topic | leaf nitrogen content hybrid method UAV hyperspectral image |
| url | https://www.mdpi.com/2072-4292/14/24/6334 |
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