RICE MONITORING AND YIELD ESTIMATION BASED ON UAV REMOTE SENSING AND SOLAR RADIATION

摘要

Crop monitoring using Unmanned Aerial Vehicle (UAV) remote sensing is an important issue and technique related to improvement of yield and quality of crops, reduction of environmental load, research on vegetation. The objective of this study was to explore applicable and simple algorithms for estimation of plant length, leaf area index (LAI) and yield of paddy rice based on UAV remote sensing and solar radiation datasets. The conclusions of this study were as follows: (1) NDVI-pure vegetation (NDVIpv) and Green-NDVI (GNDVI) showed high accuracy and applicability in plant length and LAI estimation. (2) With respect to Koshihikari, the average solar radiation of the 20-day period from the heading stage was found to have the highest correlation to yield. In the case of Fusaotome and Fusakogane, the average solar radiation of the 30-day period from the heading stage had the highest correlation to yield. (3) As a result of applying the yield estimation models to another year or location, RMSE of PAR-based model was 45.7g/m2. On the other hand, RMSE of GSR-based model was 24.8g/m2. The GSR-based model outperformed PAR-based model. The simple algorithms using UAV remote sensing and solar radiation provided in this study would work as applicable algorithms for the estimation of plant length, LAI and yield of paddy rice.