Construction of cotton leaf nitrogen content estimation  model based on the PROSPECT model

Feng XU; Yiren DING; Shizhe QIN; Hongyu WANG; Lu WANG; Yiru MA; Xin LV; Ze ZHANG; Bing  CHEN

doi:10.15835/nbha52113565

Authors

Feng XU Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Yiren DING Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Shizhe QIN Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Hongyu WANG Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Lu WANG Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Yiru MA Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Xin LV Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Ze ZHANG Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Bing CHEN Xinjing Academy of Agricultural and reclamation science, Shihezi 832003 (CN)

DOI:

https://doi.org/10.15835/nbha52113565

Keywords:

cotton, hyperspectral, leaf nitrogen content, machine learning, radiative transfer model

Abstract

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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