A cotton leaf nitrogen monitoring model based on spectral-fluorescence data fusion


  • Xiu LIN Shihezi University, College of Agriculture, The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
  • Faduman HA Xinjiang Agricultural Vocational and Technical College, Changji, 831100 (CN)
  • Lulu MA Shihezi University, College of Agriculture, The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
  • Xiangyu CHEN 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)
  • Aiqun CHEN Kekedala Agricultural Technology Popularization Station of the Fourth Division of Xinjiang Production and Construction Corps, Kekedala, 835213 (CN)
  • Zhenan HOU 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)




characteristic parameter, chlorophyll fluorescence parameters, cotton leaf nitrogen, hyperspectral imaging, monitoring model


In the present study, hyperspectral imaging and remote sensing of fluorescence were integrated to monitor the nitrogen content in leaves of drip-irrigated cotton at different growth periods in northern Xinjiang, China. Based on the spectrum and chlorophyll fluorescence parameters of nitrogen content in cotton leaves of different growth periods obtained through the shuffled frog-leaping algorithm (SFLA), the successive projection algorithm (SPA), grey relational analysis (GRA), and competitive adaptive reweighted sampling (CARS), a monitoring model of nitrogen content in cotton leaves was established via on hyperspectral imaging, chlorophyll fluorescence parameters, and spectral-fluorescence data fusion. The results showed that: (1) there were significant positive correlations between the chlorophyll fluorescence parameters Fv'/Fm', Fv/Fm, Yield, Fm, NPQ, and the nitrogen content at each growth period. (2) The effectiveness of chlorophyll fluorescence parameters in inversion of nitrogen content was the highest at the budding period and the blooming period, and the coefficients of determination (R2) of the validation sets were 0.745 and 0.709, respectively. (3) In the monitoring model for cotton leaf nitrogen in the blooming period that was established based on the decision-level algorithm and spectral-fluorescence data fusion, the R2 value of the training set reached 0.961, and that of the validation set was 0.828. In conclusion, the findings of this study suggest that the feature-level fusion and decision-level fusion algorithms of spectral-fluorescence data can effectively improve the accuracy and reliability of cotton leaf nitrogen monitoring.


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How to Cite

LIN, X., HA, F., MA, L., CHEN, X., MA, Y., CHEN, A., HOU, Z., & ZHANG, Z. (2023). A cotton leaf nitrogen monitoring model based on spectral-fluorescence data fusion. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 51(1), 13059. https://doi.org/10.15835/nbha51113059



Research Articles
DOI: 10.15835/nbha51113059