A UAV-based vegetation index dataset for assessing plant traits in major crop species

Authors: Diogo Castilho1,2*, Danilo Tedesco3, Carlos Hernandez3, Beata E. Madari1,2, Ignacio Ciampitti3* <br> 1 Graduate Program in Agronomy, Federal University of Goiás, Goiânia, GO, Brazil 2 Brazilian Agricultural Research Corporation (Embrapa Rice and Beans), Santo Antônio de Goiás, GO, Brazil. 3 Department of Agronomy, Kansas State University, 1712 Claflin Rd., Manhattan, KS, 66506, USA A new dataset was described and summarized the state of the art in drone-based plant traits assessment, resulting from a systematic search of Scopus and Web of Science databases. This dataset contains the result of extracting data of 14 plant traits (yield [t ha-1], plant N density [g m-2], plant N concentration [%], plant N accumulation [g m-2], NUE - plant N content [kg ha-1], NNI, N uptake [kg ha-1], N rate [kg ha-1], leaf N density [g m-2], leaf N content [g m-2], leaf N concentration [%], leaf N accumulation [g m-2], canopy N content [g m-2], and biomass [t ha-1]) from 41 papers with a total of 11189 observations for 11 major crop species from 13 countries around the globe. <br> Many vegetation indices derived from unmanned aerial vehicle could track the relative yield, N uptake, and leaf N concentration in wheat, cotton and rice species. This method may not only be used to evaluate the N management practices but also may have application in making in-season N management decisions. Furthermore, this dataset is relevant to develop precise and broad diagnostic of N nutritional status of wide crop species in different sites and conditions. <br> For more information related to this dataset or codes, please contact the corresponding author at: [e-mail] <br> For cite the dataset, please use: Castilho, D., Tedesco, D., Hernandez, C., Madari, E. B., Ciampitti, I. A UAV-based vegetation index dataset for assessing plant traits in major crop species. figshare https://figshare.com/s/177cdcd70963a415925b (2023).