# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 3 Results and Discussion
### Analysis of Results
#### 3.3.1 Analysis of the Results of the Single-Trial Group  
From the data analysis of 215 correlation points across five experimental groups (a, b, c, d, e), the data comparison results are shown in Figure 15. A linear regression analysis indicated that the extracted point cloud height data were highly correlated with the manually measured height data, with an R\({}^{2}\) ranging from 0.9880 to 0.9989, averaging 0.9943. The root mean square error (RMSE) ranged from 0.0148 m to 0.029 m, with an average of 0.0222 m (as shown in Table 5). The fitting line was nearly parallel to the 1:1 line, indicating a high degree of compatibility and superior accuracy of the extraction method compared to other plant height measurement methods. The k value of the fitted line parameters was close to one, indicating model stability. However, the estimated point  
Figure 14: Histogram of manually measured maize plant height data for five sets of tests. The heights for each test (**a–e**) were obtained through manual measurement, serving as a ground-truth comparison to the point cloud estimation. cloud heights were consistently lower than the manually measured heights, which can be attributed to several factors. The limitations of the point cloud extraction method, including occlusion, uneven plant surfaces, and resolution issues, may prevent an accurate capture of the true plant height. Additionally, environmental factors such as lighting, plant positioning, sensor calibration, and variability in the plant surface structure (e.g., leaves, branches, and stalk curvature) can further affect the accuracy of height extraction. Manual measurement errors, such as difficulties in locating the plant top, also contribute to these discrepancies. The differences in b values reflected error sources in the various trial groups, including measurement environment, corn surface variability, and calculation errors. Environmental factors such as lighting conditions (e.g., overexposure or shadowing) and wind speed may have introduced variability during UAV data collection, leading to slight deviations in reconstruction quality. For example, high wind speeds could cause UAV instability, affecting the alignment of multi-view images and the accuracy of the point cloud reconstruction. To minimize the impact of these factors, several strategies were implemented in this study:  
1. Environmental control during data collection: UAV flights were conducted during the early morning or late afternoon to ensure stable lighting conditions and avoid overexposure caused by midday sunlight. Flights were avoided during strong wind conditions to ensure UAV stability. Additionally, the flights were timed to avoid periods of high humidity, such as early mornings with dew or afternoons with elevated soil evaporation;
2. Flight path optimization: A tic-tac-toe flight pattern was used to ensure a 70-80% image overlap. This overlap minimized inconsistencies caused by variations in wind speed or sunlight conditions across the images;
3. Post-processing and noise correction: During point cloud processing, noise filtering techniques, such as statistical outlier removal and aggressive noise filtering in Pix4 Dmapper, were applied to correct for irregularities caused by plant movement or lighting variations;
4. Validation against ground-truth data: UAV-derived height measurements were validated against manually measured ground-truth data to quantify and adjust for any deviations introduced by environmental factors. Although these measures effectively reduced the impact of environmental factors, some sources of error remained, such as image misalignment caused by high wind speeds or reduced point cloud resolution due to uneven lighting conditions. These issues may result in slight decreases in accuracy under specific conditions. However, the overall results demonstrate that this method is highly applicable and robust for field-scale height estimation in maize populations.