Integrated diagnosis and time-series sensitivity evaluation of nutrient deficiencies in medicinal plant (Ligusticum chuanxiong Hort.) based on UAV multispectral sensors - Li, Wenbo; Wang, Ke; Han, Guiqi; Wang, Hai; Tan, Ningbo; Yan, Zhuyun (Frontiers in Plant Science, 2023)
Nitrogen(N), phosphorus(P), and potassium(K) are essential elements that are highly deficient during plant growth and timely diagnosis of deficient crops can protect their yield and quality. Near-ground remote sensing technology based on unmanned aerial vehicle (UAV) and sensor is often applied to crop growth condition monitoring and agricultural management and has been proven to be able to be used for monitoring plant N, P, and K content. However, its integrated diagnostic model has been less studied. In this study, the UAV multispectral technology was used to establish the diagnosis model of N, P and K deficiency in Ligusticum chuanxiong Hort. The optimal model variables were selected, and the effects of different sampling heights and modeling algorithms on the model efficiency under the time span were evaluated. At the same time, we evaluated the timeliness of the model based on leaf element content determination and SPAD. The results showed that the LR algorithm’s model had optimal performance at all periods and flight altitudes. The optimal accuracy of N-deficient plants identification reached 100%, P/K-deficient plants reached 92.4%, and normal plants reached 91.7%. The results of UAV multispectral diagnosis, chemical diagnosis, and SPAD value diagnosis were consistent in the diagnosis of N deficiency, and the diagnosis of P and K deficiency was slightly lagging behind that of chemical diagnosis. In conclusion, UAV multispectral technology has the feasibility for rapid and timely diagnosis of L. chuanxiong’s N, P, and K deficits. Meanwhile, the standardized production of medicinal plant resources provides new solutions.