## 2 Materials and Methods  
### Data Collection  
The rice test base of this experiment was located in the potting farm of Huazhong Agricultural University, and the rice used for the experiment was planted in plastic containers. The bottom diameter of the plastic container was 16 cm, the top diameter was 19.5 cm and the height of the container was 19 cm. Each container was loaded with 5 kg of air-dried soil, and rice seedlings were planted in it with the proper amount of water. A visible-light industrial camera (AVT Stingray FG504) was used to take pictures of rice. We obtained 424 RGB images in PNG format at a size of 2452 by 2056. This batch of data contains three growth stages, in which the first stage of rice is at the tillering stage, the second stage of rice is at the jointing stage and the third stage of rice is at the heading and grain-filling stage. The RGB images of rice obtained by camera are shown in Figure 1.  
RGB images were transformed into binary images by the threshold method, and the formula was: 2G-R-B \(\geq\) M (where G was the green component of the pixel, R was the red component and B was the blue component); M = 20 was the threshold value [29]. The binary images of the three batches of rice samples are shown in Figure 2.  
Figure 1: Sample RGB images of rice growth in three stages. (**a**) shows the tillering stage of rice; (**b**) shows the jointing stage of rice; (**c**) shows the heading and grain-filling stage of rice. The box-counting method is one of the most widely used methods to measure the fractal dimension. Its measuring principle is shown in Figure 3. The square grid with side length of \(\delta\) was overlaid with the graph to be measured, and then the number of grids \(N(\delta)\) that overlapped with the boundary curve was calculated. Then, we continuously reduced the side length of the grid, and we obtained the number of overlapping grids at different scales \(\delta\). The relationship between the number of overlapped grids \(N(\delta)\) and the grid side length \(\delta\) satisfied Equation (1). \[\log N(\delta)=D\log\frac{1}{\delta}+k \tag{1}\]  
In Equation (1), \(k\) is a constant and \(D\) is the fractal dimension. Then, the slope of the straight-line equation is the estimated value of the box-counting dimension. Besides the fractal dimension, some traditional rice representations could be extracted based on the binary images of rice. By using the characterization extraction system, we  
Figure 3: Box-counting method. (**a**) Cover the image with big boxes; (**b**) cover the image with small boxes. Figure 2: Sample binary graphs of rice growth in three stages. (**a**) shows the tillering stage of rice; (**b**) shows the jointing stage of rice; (**c**) shows the heading and grain-filling stage of rice. could obtain the characteristics of rice, including width, leaf area and texture characteristics, etc. Feature extraction was carried out once for each binary image. The characteristic variables and their abbreviations which were obtained from the images of rice are shown in Table 1.  
'PW' is the width of the rice plant. 'PH(V)' is the vertical height of the rice (the highest height of the rice in its natural state). 'PH' is the height of rice leaves after straightening and the process is simulated by image-processing method in the program. 'SA' is the lateral projection area of the rice plant. 'IFD' is the fractal dimension of the rice binary image obtained by box-counting method. 'SFD' is the box-counting fractal dimension based on the minimum bounding rectangle of the binary image. 'G_g' is a texture feature (gradient information), and we found that it could reflect the number of panicles in rice. 'f1-f12' are relative frequencies and 'LD1-LD6' are structural parameters. They all reflect the compactness of the plants. If their values are large, the plant is compact. In addition to the above rice characterization data, the fresh weight (g), dry weight (g) and plant height (cm) of rice could be obtained by manual measurement.