Study on the nitrogen content estimation model of cotton leaves based on “image-spectrum-fluorescence” data fusion - Qin, Shizhe; Ding, Yiren; Zhou, Zexuan; Zhou, Meng; Wang, Hongyu; Xu, Feng; Yao, Qiushuang; Lv, Xin; Zhang, Ze; Zhang, Lifu (Frontiers in Plant Science, 2023)
Objective: Precise monitoring of the nitrogen content of cotton leaves is important for increasing yield and reducing fertilizer application. Spectra and images are used to monitor crop nitrogen information. However, nitrogen information monitoring based on a single data source cannot consider the expression of phenotypic and physiological parameters simultaneously, which can affect the accuracy of inversion. Introducing a multi-source data-fusion mechanism can improve the accuracy and stability of cotton nitrogen content monitoring from the perspective of information complementarity. Methods: Five nitrogen treatments were applied to the test crop, Xinluzao No. 53 cotton, grown in indoors. Cotton leaf hyperspectral, chlorophyll fluorescence, and digital image data were collected and screened, and a multilevel data-fusion model combining multiple machine learning and stacking integration learning was built from three dimensions: feature-level fusion, decision-level fusion, and hybrid fusion. Results: The determination coefficients (R2) of the feature-level fusion, decision-level fusion, and hybrid-fusion models were 0.752, 0.771, and 0.848, and the root-mean-square errors (RMSE) were 3.806, 3.558, and 2.898, respectively. Compared with the nitrogen estimation models of the three single data sources, R2 increased by 5.0%, 6.8%, and 14.6%, and the RMSE decreased by 3.2%, 9.5%, and 26.3%, respectively. Conclusion: The multilevel fusion model can improve accuracy to varying degrees, and the accuracy and stability were highest with the hybrid-fusion model; these results provide theoretical and technical support for the optimization of an accurate method of monitoring cotton leaf nitrogen content.