Abstract
Recently, food security has become a major world-wide concern. The development of a stable food supply system is necessary. Therefore, the practice of precision agriculture has received a great deal of attention. The management of crop growth is one of the most important aspects of precision agriculture, and it requires the accurate monitoring of the spatial distribution of the leaf area index (LAI) of target crops at the field scale. In recent years, unmanned aerial vehicles (UAVs) have been used to monitor the spatial distribution of the LAI of target crops at the field scale. The use of UAVs for LAI monitoring is expected to contribute to the efficiency of farming. However, no method for accurately estimating the spatial distribution of LAI using UAV has been established yet. In this study, several empirical regression models for estimating the LAI of paddy rice from several vegetation indices derived from UAV images were compared to help establish a precise estimation method of the spatial distribution of the paddy rice LAI at the field scale. As a result, although the estimation accuracy was not very high, this study indicated that vegetation indices that can consider the effect of vegetation density and whose estimation accuracy of LAI does not depend on the data used for calculating regression equations, such as the time-series change index of plant structure (TIPS), had higher potential to estimate the spatial distribution of the LAI of paddy rice than other vegetation indices that cannot consider the effect of vegetation density.
