# Rapid Detection of Key Phenotypic Parameters in Wheat Grains Using Linear Array Camera
## 3 Results  
Using the verification method and analytical indicators developed in Section 2.4, the fitting effect diagram shown in Figure 17 was obtained. By comparing phenotypic parameters from software-based measurement with those from manual measurement, the determination coefficients (\(R^{2}\)) for both grain length and width were found to be above 0.9, and only the root mean square error of xm35 exceeded 0.1. Among the determination coefficients \(R^{2}\) of the grain weight, the effects of hm20 and hhm237 were relatively poor, while the other three reached above 0.8. The root mean square error was in the range of 2.3 to 3.9, which was larger than the errors for the grain length and width. The \(R^{2}\) values of the grain length, width, and weight under the summarized results were 0.986, 0.9505, and 0.7635, respectively, whereas the corresponding root mean square errors were 0.0887, 0.0541, and 3.8329, respectively. Figure 16: Precise positioning of the germination point. Figure 17: _Cont._To clearly present the comparison results, the mean error values and average error percentages for wheat grain length, width, and weight are summarized in Table 1. Regarding grain length, the highest mean error was observed in xm35, reaching 0.0914 mm, corresponding to an average error of 1.392%, while nhm237 exhibited the lowest mean error of 0.051 mm with an average of 0.675%. The overall mean error for grain length across all varieties was 0.066 mm, with an average error of 0.932%. For grain width, hm44 exhibited the largest mean error at 0.050 mm, with an average of 1.508%, whereas nhm237 had the smallest mean error of 0.036 mm and an average of 1.098%. The overall mean grain width error was 0.043 mm, corresponding to an average of 1.254%. In terms of grain weight, nhm237 showed the highest mean error of 3.24 mg, with an average of 7.606%, while xm35 demonstrated the lowest mean error at 1.67 mg and an average of 3.831%. The overall mean error for grain weight was 3.15 mg, with an average of 7.142%. Collectively, the error patterns vary between wheat varieties for grain length, width, and weight. nhm237 exhibited relatively low errors in grain length and width but comparatively high errors in grain weight. However, xm35 showed larger errors in grain length and width but a smaller error in grain weight. The varieties hm20, hm44, and nhm432 showed moderate error levels across all measured parameters. These results indicate that the wheat grain data acquisition method proposed in this study demonstrates a high level of consistency with manual measurements, suggesting that it can reliably meet the accuracy requirements necessary for wheat variety testing. To determine the direction of the bud point, shape, roundness, texture, and pixel grayscale values were experimentally analyzed in the early image processing code. During the experiment, there were several special grains in terms of shape differences, the roundness of endpoints, and texture features at both ends. Through comparison, a determination based on the pixel mean value was found to have the highest accuracy rate. Table 2 shows  
Figure 17: Comparison of software and manual measurements and related linear fitting: (a) hm20 scatter plot; (b) hm44 scatter plot; (c) nhm237 scatter plot; (d) nhm432 scatter plot; (e) xm35 scatter plot; (f) summary of scatter plots. that the overall accuracy rate of determining the direction of the bud points of different wheat grain varieties based on a pixel mean value is 97.5%, proving that the method has a certain degree of feasibility. Further experimental analysis of bud point positioning can be carried out based on this method. Based on the bud-point direction determination, the bud-point coefficient, k, was determined using images of photographed wheat grains. A small subset of wheat grains was selected for germination cultivation, and images were captured both before and after germination. The actual germination point positions were manually selected within the software based on the post-germination images.