Modelling of nitrogen content estimation in cotton based on UAV ‘Spectrum-Image’ data fusion

Nitrogen is crucial for crop growth, development, yield, and quality. Traditional nutrition monitoring relies on single data sources; however, spatial coverage and information limitations hamper the accuracy of such monitoring methods. The recently developed unmanned aerial vehicle (UAV) remote sensing technology has emerged as an efficient and convenient method of crop nutrition monitoring, which allows the integration of data from sources, such as hyperspectral and digital images, resulting in comprehensive and multi‐angular insights. This study is aimed at enhancing crop monitoring accuracy by integrating multiple data types obtained at the UAV scale, using ‘Xinluzao 53’ cotton as an experimental subject. Nitrogen content was obtained via hyperspectral and digital imaging and the features of the two data sources analyzed by constructing four machine learning models: Ridge (RR), back-propagation neural network (BPNN), random forest (RF), and Bagging, which were integrated with the multilevel data fusion methods to obtain nutrition information. The results indicated optimal efficacy for RF together with the UAV ‘spectrum-image’ feature-level fusion framework, with a validation set R2 of 0.915 and RMSE of 1.562, while the optimal decision-level fusion framework was found to be Bagging, with a validation set R2 of 0.923 and RMSE of 1.488. UAV-based ‘spectral-image’ multilevel fusion frameworks were found to enhance the accuracy of monitoring, with the optimum decision-level fusion evaluation indices providing crucial theoretical support for precision agriculture in the future.