# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 3 Results and Discussion
### Analysis of Results
#### 3.3.2 Analysis of the Results of the Composite Test Group  
The five experimental groups in this study were conducted in two fields and three different maize plant environments, referred to as fields 1 and 2 and maize plants a, b, and c. The composite test groups are specified as shown in Table 6. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Test No.** & **Field Number** & **Corn Plant Number** \\ \hline a & 1 & a \\ b & 1 & b \\ c & 1 & b \\ d & 2 & c \\ e & 2 & c \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Distribution of composite test groups. Figure 15: Comparative validation plot of plant height for the five experimental groups. The composite test group involved two fields and three different maize plant environments, consisting of four sets of trials: trials 1 and 2, conducted with the same maize plant in the same field; trial 3, conducted with a different maize plant in the same field; and trial 4, conducted with a different plant in a different field, as shown in Figure 16 and Table 7.  
For tests 1 and 2, the R\({}^{2}\) under the same maize plant condition in the same field were all above 0.99, and the RMSE was below 0.0274 m, indicating that the data were extremely correlated and that the error was smaller than that of a single test group. For test 3, the R\({}^{2}\) was 0.91, the RMSE was 0.121 under different maize plants in the same field, and the correlation was weaker than that of tests 1 and 2 but still maintained high accuracy. The results showed that the model had high feasibility. For experiment 4, the lowest R\({}^{2}\) was 0.8884 and the largest RMSE was 0.1692 under different plant conditions in different fields. But, there was still a strong correlation, and the error was at the decimeter level, indicating that the model has high feasibility, but with a large error. The main reasons for the correlation differences include the effects of environmental and temporal differences (e.g., light, wind speed, etc.) on the point cloud reconstruction, and the error superposition effect under different test conditions. Although the correlation and precision of the composite test group were lower than those of the single test group, the correlation and precision were close to those of the single test group under the condition of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test No.** & **Associated Points** & **Equation for Estimating Plant Height** & **R\({}^{2}\)** & **RMSE** \\ \hline a & 59 & 1.023x + 73.71 & 0.9989 & 0.0163 \\ bc & 83 & 0.9842x + 52.63 & 0.99347 & 0.0274 \\ de & 77 & 1.017x + 89.57 & 0.9956 & 0.0265 \\ abc & 142 & 0.9825x + 62.74 & 0.91889 & 0.1210 \\ abcde & 215 & 1.118x + 61.79 & 0.8884 & 0.1692 \\ \hline \hline \end{tabular} Note. x corresponds to the manually measured plant height.  
\end{table} 
Table 7: Linear statistics for the composite test group situation. Figure 16: Validation plot of plant height comparison for composite test groups. the same maize plant in the same field, which indicated that the method of this study was highly reliable for field application under specific conditions. For instance, during the field tests conducted under strong sunlight, overexposure may have caused a loss of fine details in the images, reducing point cloud resolution. Similarly, dense canopy structures in later maize growth stages, such as at maturity, may introduce shading effects that hinder the accurate segmentation of individual plants. Conversely, in early growth stages with sparser canopies, noise introduced by weeds or soil may impact segmentation accuracy. These factors demonstrate that the method's accuracy can vary depending on the environmental conditions and maize growth stages. Although the correlation and precision of the composite test group were lower than those of the single test group, the correlation and precision were close to those of the single test group under the condition of the same maize plant in the same field. This indicated that the method of this study was highly reliable for field application under specific conditions.