Estimating potassium in potato plants based on multispectral images acquired from unmanned aerial vehicles

YanPeng Ma; ZhiChao Chen; YiGuang Fan; MingBo Bian; GuiJun Yang; RiQiang Chen; HaiKuan Feng

doi:10.3389/fpls.2023.1265132

Estimating potassium in potato plants based on multispectral images acquired from unmanned aerial vehicles

Front Plant Sci. 2023 Sep 21:14:1265132. doi: 10.3389/fpls.2023.1265132. eCollection 2023.

Authors

YanPeng Ma^{1

2}, ZhiChao Chen¹, YiGuang Fan², MingBo Bian^{1

2}, GuiJun Yang², RiQiang Chen², HaiKuan Feng^{2

3}

Affiliations

¹ School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China.
² Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
³ National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, China.

Abstract

Plant potassium content (PKC) is a crucial indicator of crop potassium nutrient status and is vital in making informed fertilization decisions in the field. This study aims to enhance the accuracy of PKC estimation during key potato growth stages by using vegetation indices (VIs) and spatial structure features derived from UAV-based multispectral sensors. Specifically, the fraction of vegetation coverage (FVC), gray-level co-occurrence matrix texture, and multispectral VIs were extracted from multispectral images acquired at the potato tuber formation, tuber growth, and starch accumulation stages. Linear regression and stepwise multiple linear regression analyses were conducted to investigate how VIs, both individually and in combination with spatial structure features, affect potato PKC estimation. The findings lead to the following conclusions: (1) Estimating potato PKC using multispectral VIs is feasible but necessitates further enhancements in accuracy. (2) Augmenting VIs with either the FVC or texture features makes potato PKC estimation more accurate than when using single VIs. (3) Finally, integrating VIs with both the FVC and texture features improves the accuracy of potato PKC estimation, resulting in notable R ² values of 0.63, 0.84, and 0.80 for the three fertility periods, respectively, with corresponding root mean square errors of 0.44%, 0.29%, and 0.25%. Overall, these results highlight the potential of integrating canopy spectral information and spatial-structure information obtained from multispectral sensors mounted on unmanned aerial vehicles for monitoring crop growth and assessing potassium nutrient status. These findings thus have significant implications for agricultural management.

Keywords: fraction vegetation coverage; multispectral imagery; plant potassium content; potato; texture feature; vegetation index.

Grants and funding

The authors declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Key Project of the National Natural Science Foundation of China (41601346,U22A20620,U21A20108), the Heilongjiang Province Unveiled and Hanged Science and Technology Research Project (2021ZXJ05A05) and Doctoral Science Foundation of Henan Polytechnic University (Grant: B2021-20).