Monitoring Indicators for Comprehensive Growth of Summer Maize Based on UAV Remote Sensing

Maize is one of the important grain crops grown globally, and growth will directly affect its yield and quality, so it is important to monitor maize growth efficiently and non-destructively. To facilitate the use of unmanned aerial vehicles (UAVs) for maize growth monitoring, comprehensive growth indicators for maize monitoring based on multispectral remote sensing imagery were established. First of all, multispectral image data of summer maize canopy were collected at the jointing stage, and meanwhile, leaf area index (LAI), relative chlorophyll content (SPAD), and plant height (VH) were measured. Then, the comprehensive growth monitoring indicators CGMI_CV and CGMI_CR for summer maize were constructed by the coefficient of variation method and the CRITIC weighting method. After that, the CGMI_CV and CGMI_CR prediction models were established by the partial least-squares (PLSR) and sparrow search optimization kernel extremum learning machine (SSA-KELM) using eight typical vegetation indices selected. Finally, a comparative analysis was performed using ground-truthing data, and the results show: (1) For CGMI_CV, the <i>R</i>² and RMSE of the model built by SSA-KELM are 0.865 and 0.040, respectively. Compared to the model built by PLSR, <i>R</i>² increased by 4.5%, while RMSE decreased by 0.3%. For CGMI_CR, the <i>R</i>² and RMSE of the model built by SSA-KELM are 0.885 and 0.056, respectively. Compared to the other model, <i>R</i>² increased by 4.6%, and RMSE decreased by 2.8%. (2) Compared to the models by single indicator, among the models constructed based on PLSR, the CGMI_CR model had the highest <i>R</i>². In the models constructed based on SSA-KELM, the <i>R</i>² of models by the CGMI_CR and CGMI_CV were larger than that of the models by SPAD (<i>R</i>² = 0.837), while smaller than that of the models by LAI (<i>R</i>² = 0.906) and models by VH (<i>R</i>² = 0.902). In summary, the comprehensive growth monitoring indicators prediction model established in this paper is effective and can provide technical support for maize growth monitoring.