## 1 Introduction
They used an exponential smoothing method to calculate the trend yield of rice, and conducted correlation analysis with the monthly average temperature, maximum temperature, minimum temperature, sunshine hours and precipitation in the rice growth period. The key factors affecting the yield of rice were determined, and the regression equation was established and tested. The average accuracy of the prediction model was up to 96.2%. [PERSON] et al. (2019) studied climate change in different climatic zones, different climatic conditions and recent years. Combined with the variation trend of rice yield in different climatic zones, they analyzed the correlation between rice yield and climatic factors, and selected the main controlling factors of rice yield in each climatic zone. The regression model was established to forecast the production of different climate zones in the next five years, and the results are relatively stable. Meteorological models need a lot of meteorological data and yield data, and need to maintain the consistency of growth conditions. Therefore, the established models have poor generality and are difficult to be popularized. The prediction based on remote sensing and spectral model prediction is mainly to obtain the plant spectral index through a multi-spectral camera, select key factors and establish a prediction model based on yield data ([PERSON] et al., 2019). [PERSON] (2019) extracted vegetation index, end element abundance, texture features and other information by using ground hyperspectral data and multi-spectral data of the UAV platform. Rice yield estimation was studied by stepwise linear regression, BP neural network and random forest algorithm. [PERSON] et al. (2019) obtained the normalized vegetation index (NDVI) of remote sensing data according to time series and evaluated it with Pearson product moment correlation coefficient (Pearson) of average rice yield in the field by comparing the mean value of NDVI combination in different time periods. NDVI data were used to establish several prediction models with rice yield, and the best model was selected. Remote sensing yield estimation is generally applicable to large-area yield estimation, but when the planting area is not large enough, the accuracy is often reduced, and it is difficult to obtain remote sensing data. The prediction is made based on the image feature model, mainly through segmentation of RGB images, extraction of features and establishment of regression model ([PERSON] et al., 2019). [PERSON] and [PERSON] (2019) made a preliminary study on the relationship between texture features of wheat spike head image and yield by using MATLAB image-processing technology. The mathematical model of spike head image texture and yield was established by using multiple linear regression method, and 84.42% of the samples with an accuracy of more than 15% were measured by using the established model. [PERSON] et al. (2019)performed the segmentation of wheat ears through color space conversion and image-processing technology to identify the number of ears. By predicting the number of grains by panicle area and combining this with 1000-grain weight, they built a model to predict the wheat yield per unit area with an average accuracy of more than 90%. The image feature model can not only make predictions with damage, but also make predictions without damage, and the effect is better when the planting area is small. Fractal dimension is an important image feature, but it is seldom used in rice yield estimation. The purpose of this study is to combine the characteristics of rice in the early growth stage with the fractal dimension; practical and low-cost models are established. These models can be used to predict the fresh weight, dry weight and plant height of rice, which need to be measured by machine and are highly correlated with yield. The model can not only provide a reference for countries to formulate food security strategies, but also help to measure the yield of smart agriculture, so this study has a strong practical value.