Research on Empirical Model and Gap Rate Model for Estimating Rice Leaf Area Index Based on UAV HD Digital Images

The Leaf Area Index (LAI), as an important plant characteristic parameter, is of great significance for the monitoring of vegetation growth and the estimation of surface vegetation productivity. Rice is one of the world's major food crops, timely and accurate measurement of rice LAI can provide scientific information on agriculture. The remote sensing system of UAV is characterized by its cost-effective and real-time data acquisition. The method of estimating LAI by remote sensing technology has great advantages over traditional methods and has gradually become a frontier method for agricultural research. At present, the commonly used LAI inversion methods are empirical model method and physical model method. The former is not accurate because not all of the spectral information is used. The latter cannot directly calculate the analytical solution because of the complicated model and many input parameters. The main purpose of this paper is to obtain high-definition digital images of different varieties of rice using low-altitude drones, and to analyze the feasibility of estimating the LAI of rice canopy by empirical model method and porosity model method, and analyze the difference and estimation process between them. There are problems.