Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model
Leaf nitrogen content (LNC) is an important index to measure the nitrogen deficiency in cotton. The rapid and accurate monitoring of LNC is of great significance for understanding the growth status of cotton and guiding precise fertilization in the field. At present, the hyperspectral technology mo...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AcademicPres
2024-02-01
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| Series: | Notulae Botanicae Horti Agrobotanici Cluj-Napoca |
| Subjects: | |
| Online Access: | https://www.notulaebotanicae.ro/index.php/nbha/article/view/13565 |
| _version_ | 1797231360005373952 |
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| author | Feng XU Yiren DING Shizhe QIN Hongyu WANG Lu WANG Yiru MA Xin LV Ze ZHANG Bing CHEN |
| author_facet | Feng XU Yiren DING Shizhe QIN Hongyu WANG Lu WANG Yiru MA Xin LV Ze ZHANG Bing CHEN |
| author_sort | Feng XU |
| collection | DOAJ |
| description |
Leaf nitrogen content (LNC) is an important index to measure the nitrogen deficiency in cotton. The rapid and accurate monitoring of LNC is of great significance for understanding the growth status of cotton and guiding precise fertilization in the field. At present, the hyperspectral technology monitoring of LNC is very mature, but it is interfered with by external factors such as shadow and soil, and data acquisition is still dependent on manpower. Therefore, on the basis of clarifying the correlation and quantitative relationship between physiological parameters and cotton LNC, the 400-2500 nm spectral curve was simulated based on PROSPECT-5 model. Combined with the measured spectra, the sensitive bands of leaf nitrogen content were screened, and four machine learning algorithms based on the reflectance of the sensitive bands were compared to construct a model for the estimation of LNC in cotton and determine the optimal model. The results show the following: (1) The parameter with the best correlation with nitrogen content was Cab, and the linear relationship was y=0.3942x+12.521, R2=0.81, RMSE=12.87 g/kg. (2) The shuffled frog leaping algorithm (SFLA) and the successive projections algorithm (SPA) were used to screen the relevant bands sensitive to LNC. SFLA selected nine characteristic bands, mainly distributed between 700 and 750 nm. SPA screened seven characteristic bands, mainly distributed between 670 and 760 nm. The characteristic bands of both screening methods were distributed near the red edge. (3) Based on the sensitive bands, the four machine learning algorithms were compared. Among them, the band modeling of SFLA screening under the random forest (RF) algorithm was the best (modeling set R2=0.973, RMSE=1.001 g/kg, rRMSE=3.41%, validation set R2=0.803, RMSE=3.191 g/kg, rRMSE=10.85%). In summary, this study proposes an optimal estimation model of cotton leaf nitrogen content based on the radiative transfer model, which provides a theoretical basis for the dynamic, accurate, and non-destructive monitoring of cotton leaf nitrogen content.
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| first_indexed | 2024-04-24T15:43:08Z |
| format | Article |
| id | doaj.art-bd8c856658b244ceb892c1122a81949d |
| institution | Directory Open Access Journal |
| issn | 0255-965X 1842-4309 |
| language | English |
| last_indexed | 2024-04-24T15:43:08Z |
| publishDate | 2024-02-01 |
| publisher | AcademicPres |
| record_format | Article |
| series | Notulae Botanicae Horti Agrobotanici Cluj-Napoca |
| spelling | doaj.art-bd8c856658b244ceb892c1122a81949d2024-04-01T18:49:57ZengAcademicPresNotulae Botanicae Horti Agrobotanici Cluj-Napoca0255-965X1842-43092024-02-0152110.15835/nbha52113565Construction of cotton leaf nitrogen content estimation model based on the PROSPECT modelFeng XU0Yiren DING1Shizhe QIN2Hongyu WANG3Lu WANG4Yiru MA5Xin LV6Ze ZHANG7Bing CHEN8Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003Xinjing Academy of Agricultural and reclamation science, Shihezi 832003 Leaf nitrogen content (LNC) is an important index to measure the nitrogen deficiency in cotton. The rapid and accurate monitoring of LNC is of great significance for understanding the growth status of cotton and guiding precise fertilization in the field. At present, the hyperspectral technology monitoring of LNC is very mature, but it is interfered with by external factors such as shadow and soil, and data acquisition is still dependent on manpower. Therefore, on the basis of clarifying the correlation and quantitative relationship between physiological parameters and cotton LNC, the 400-2500 nm spectral curve was simulated based on PROSPECT-5 model. Combined with the measured spectra, the sensitive bands of leaf nitrogen content were screened, and four machine learning algorithms based on the reflectance of the sensitive bands were compared to construct a model for the estimation of LNC in cotton and determine the optimal model. The results show the following: (1) The parameter with the best correlation with nitrogen content was Cab, and the linear relationship was y=0.3942x+12.521, R2=0.81, RMSE=12.87 g/kg. (2) The shuffled frog leaping algorithm (SFLA) and the successive projections algorithm (SPA) were used to screen the relevant bands sensitive to LNC. SFLA selected nine characteristic bands, mainly distributed between 700 and 750 nm. SPA screened seven characteristic bands, mainly distributed between 670 and 760 nm. The characteristic bands of both screening methods were distributed near the red edge. (3) Based on the sensitive bands, the four machine learning algorithms were compared. Among them, the band modeling of SFLA screening under the random forest (RF) algorithm was the best (modeling set R2=0.973, RMSE=1.001 g/kg, rRMSE=3.41%, validation set R2=0.803, RMSE=3.191 g/kg, rRMSE=10.85%). In summary, this study proposes an optimal estimation model of cotton leaf nitrogen content based on the radiative transfer model, which provides a theoretical basis for the dynamic, accurate, and non-destructive monitoring of cotton leaf nitrogen content. https://www.notulaebotanicae.ro/index.php/nbha/article/view/13565cottonhyperspectralleaf nitrogen contentmachine learningradiative transfer model |
| spellingShingle | Feng XU Yiren DING Shizhe QIN Hongyu WANG Lu WANG Yiru MA Xin LV Ze ZHANG Bing CHEN Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model Notulae Botanicae Horti Agrobotanici Cluj-Napoca cotton hyperspectral leaf nitrogen content machine learning radiative transfer model |
| title | Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model |
| title_full | Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model |
| title_fullStr | Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model |
| title_full_unstemmed | Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model |
| title_short | Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model |
| title_sort | construction of cotton leaf nitrogen content estimation model based on the prospect model |
| topic | cotton hyperspectral leaf nitrogen content machine learning radiative transfer model |
| url | https://www.notulaebotanicae.ro/index.php/nbha/article/view/13565 |
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