## 1 Introduction
It provides a new method for researchers to solve traditional nonlinear problems more accurately. The fractal dimension has been widely used in various fields. [PERSON] et al. [14] proposed an automatic prediction method for sudden cardiac death (SCD) based on a fractal dimension algorithm and a fuzzy logic system. In the paper, five kinds of fractal dimensions were used for experimental research. The results show that the method of basic fractal dimension could predict SCD events, and the prediction time was up to 60 min before the onset, with an accuracy of 91.54%. [PERSON] et al. [15] proposes an improved DBC (IMDBC) to estimate the fractal dimension of three-dimensional (3D) pavement texture images based on a grid displacement mechanism. Combined with the contact characteristics of the road surface and the tire, the most suitable road surface with fractal texture was determined by the fractal stratification method. Compared with traditional DBC, the fitting accuracy of IMDBC is improved by 18.8% (full texture) and 900% (partial texture), respectively. [PERSON] et al. [16] analyzed all the images using the FracLAC algorithm in the ImageJ computing environment to obtain the box fractal dimension results. They found that computer-aided diagnostic algorithms can benefit from box fractal dimension data; the cutoff value of the fractal dimension of the specific box produced 0-99% specificity in the diagnosis of breast cancer. [PERSON] et al. [17] aimed to evaluate longitudinal changes in brain fractal geometry and its predictive value for disease progression in patients with multiple sclerosis (MS). The box number method was used to calculate the dimensionality of brain differentiation and the space between brain regions. Fractal geometric analysis of brain MRI found that patients had an increased risk of disability over the next 5 years. [PERSON] et al. [18] used pore size distribution curves and pore volume histograms to qualitatively analyze the pore structure before and after freeze-thaw. A fractal model was used to characterize pore distribution. A micro freeze-thaw damage model with fractal dimension as an independent variable was established, and the relationship between the damage parameters calculated by the model and durability factors was analyzed. [PERSON] et al. (2019) aimed to evaluate the value of fractal dimensions in differentiating benign and malignant HCGS endometrium in liquid cervical specimens. They suggest that fractal dimension analysis is an effective tool to distinguish between different types of cell groups. It is concluded that the fractal dimension detection of cervical cell carcinoma has high sensitivity and can be used as an effective screening method for differentiating benign and malignant cervical cell carcinoma. When the object is relatively complex, it can be studied from the perspective of fractals. The fractal dimension can reflect the complexity of the object, which may improve the analysis effect. The traditional method of yield estimation is field sampling survey. Observers estimate the yield of a large area according to the growth condition of samples through observation sampling evaluation. This estimation method cannot be standardized and requires a large amount of manpower and material resources. At present, rice yield prediction is mainly divided into meteorological model prediction, remote sensing model prediction and image feature model prediction. Forecasting based on a meteorological model is mainly to analyze the correlation between meteorological factors and yield. The key meteorological factors are selected to establish a model for yield prediction ([PERSON] and [PERSON], 2019). In order to explore the influence of meteorological factors on rice yield, [PERSON] et al. (2019) used meteorological data and rice yield data of Tongxiang city over 13 years.