Integrated diagnosis and time-series sensitivity evaluation of nutrient deficiencies in medicinal plant (Ligusticum chuanxiong Hort.) based on UAV multispectral sensors

Abstract

BackgroundNitrogen(N), phosphorus(P), and potassium(K) are essential elements that are highly deficient during plant growth. Existing diagnostic methods are not suitable for rapid diagnosis of large-scale planting areas. Near-ground remote sensing technology based on unmanned aerial vehicle (UAV) and sensor is often applied to crop growth condition monitoring and agricultural management. It has been proven to be used for monitoring plant N, P, and K content. However, its integrated diagnostic model has been less studied.MethodsIn this study, we collected UAV multispectral images of Ligusticum chuanxiong Hort. in different periods of nutritional stress and constructed recognition models with different heights and algorithms. 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. It was also validated in field crop production.ResultsThe 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.ConclusionsThis research uses UAV remote sensing technology to establish an efficient, fast, and timely nutritional diagnosis method for L. Chuanxiong, which is applied in production. Meanwhile, the standardized production of medicinal plant resources provides new solutions.

Effects of Biochar and Straw Amendment on Soil Fertility and Microbial Communities in Paddy Soils

“…The current study found that the value of FI was greater than 1.9 under all treatments, suggesting that microbial metabolism provides the majority of DOM [ 56 ]. Moreover, the value of the BIX lower than 1 under all treatments indicated the autochthonous or biological production of DOM components sourced from terrestrial DOM [ 57 ]. In addition, the FI decreased with the biochar and straw treatment ( Table 3 ).…”

Correlation between Spring Wheat Physiological Indicators and UAV Digital Image Index in Hetao Irrigation Area

“…Analyzing image characteristics facilitated the monitoring, diagnosis, and comprehension of plant growth status, enabling the accurate regulation and management of plant growth [1]. Several studies showed that crop quantitative remote sensing was effective in reflecting actual nitrogen levels and monitoring nitrogen across various cereal crops [2][3][4]. Mullan [5] used a digital image analysis tool for high-throughput screening of four large wheat populations to validate the relationship between digital image analysis and measures of the normalized difference vegetation index (NDVI), leaf area index, and light penetration through the crop canopy.…”