Study on selecting Vegetation Indices todetermine potassium content in rice plants using UAV multispectral imagery

Abstract

Potassium is one of the essential nutrients for the metabolism and development of rice plants, enhancing photosynthesis and disease resistance. The objective of this paper is to select the best vegetation index from the spectral bands of UAV imagery to estimate the leave potassium (K) content in rice plants. Multispectral UAV were used to collect data in rice-growing areas at three different stages: tillering (DN), heading (TB), and ripening (CS). At the same time the images were captured, three leaf samples were taken from three different positions in each field plot to determine the K content in the rice leaves in the laboratory. The vegetation indices selected in this paper include RVI, SIPI, and NDVI, which are highly correlated with the measured leaf K content, with correlation values (R) of 0.735, 0.729, and 0.722, respectively. The reliability of the K content estimation results is high, with an RMSE value ofup to 0.27%. The K content in rice plants differs at the DN, TB, and CS stages and decreases over time. The K content also varies between the two rice varieties TBR225 and J02. The results of this paper provide a necessary basis for selecting UAV technology to monitor and choose effective fertilization solutions in rice production