Estimation of Rice Basic Seedling Number Based on Mixed Pixel Decomposition

Abstract

The basic seedling number is an important basis to reflect the health level of rice. Accurately estimating the basic seedling number at tillering stage can guide the fertilizer and nitrogen amount in later stage, so as to regulate the optimal tillering number of rice. At the same time, it is of great significance for rice growth monitoring and yield forecasting. Considering that traditional manual field statistics on the number of basic seedlings are time-consuming and costly, this experiment took rice at tillering stage in Zhenjiang Runguo Farm, affiliated farm of Jiangsu University, as the research object, and used DJI UAV (M600 Pro) equipped with multi-spectral camera (Rededge-MX) to obtain multi-spectral data of rice at tillering stage. After image splicing, radiometric correction, geometric correction and other pretreatment operations were carried out on the original image, the soil end elements and vegetation end elements were extracted according to the pixel purity coefficient, and the spectral library was established. Then the mixed pixel decomposition was performed according to the fully constrained least square method, and the regression model of vegetation coverage and the number of basic rice seedlings was constructed. The model determination coefficient obtained by this method was 0.891, and the root mean square error RMSE was 4.6 plants/m2. In the traditional pixel dichotomy model (based on NDVI, VDVI and GNDVI vegetation index), the determination coefficients of R2 were 0.834, 0.744 and 0.642, and the RMSE were 5.7 plants/m2, 7.1 plants/m2 and 8.4 plants/m2. The experimental results showed that the evaluation indexes of the model based on the hybrid pixel decomposition method were superior to the pixel dichotomy model. The statistical accuracy of rice basic seedlings can be effectively improved based on the decomposition of mixed pixel decomposition, and the inverse map of rice basic seedling number is generated, which can directly count the basic seedling number and provide guidance for rice seedling replacement and thinning at tillering stage.

Keywords: rice;basic seedlings;decomposition of mixed pixels;completely constrained least squares;pixel dichotomy