# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 4 Conclusions  
In this study, we conducted a systematic investigation of plant height measurement for maize populations at the field scale using the UAV-based structure from motion-multi-view stereo technique, and reached the following main conclusions:  
1. Data acquisition and 3D reconstruction: We designed an image acquisition method suitable for maize populations in the field, utilized a drone to capture multi-view image sequences, and successfully reconstructed a dense 3D point cloud using the SFM-MVS method. This established a solid foundation for point cloud segmentation and plant height measurement;
2. Point cloud processing and segmentation: Through the processes of cropping, filtering, de-landing, and down-sampling for point cloud pre-processing, we propose a column space approximation segmentation algorithm. This effectively addresses the segmentation challenges posed by plant shading and the branching complexity of maize populations in the field, enabling high-precision segmentation of individual plants;
3. Plant height measurement and modeling: We proposed a plant height extraction method based on a bounding box and verified the high degree of consistency between the plant height extracted from the point cloud and the manually measured data through linear regression analysis. In multi-group tests, the average fitting coefficient (R\({}^{2}\)) for single tests reached 0.9943, with a root mean square error (RMSE) of less than 0.029 m. For the composite tests, the average R\({}^{2}\) was 0.9591, and the average RMSE was 0.1014 m. These results indicate that the method exhibits excellent measurement accuracy and applicability. In summary, the point cloud processing and segmentation method based on multi-view geometric 3D reconstruction technology provides a feasible approach for the efficient and accurate measurement of phenotypic parameters of crop populations at the field scale. This study offers technical support for developing maize plant height estimation models and advancing digital agriculture and contributes to the promotion of unmanned crop phenotype monitoring systems in agriculture. Moreover, the findings of this study provide important insights for the design of future unmanned crop monitoring systems. As to the hardware requirements, the results underscore the importance of using UAVs equipped with high-resolution cameras and stable flight systems to ensure accurate data collection under varying environmental conditions. Future systems may also benefit from incorporating advanced sensors such as LiDAR or multispectral cameras for enhanced data quality in dense canopy conditions or complex terrains. As to the operational protocols, the findings emphasize the need for optimized flight path planning (e.g., ensuring adequate image overlap and appropriate flight altitudes) and robust data-processing algorithms to handle environmental variability. These protocols can be adapted to suit different geographical regions with distinct environmental challenges. By addressing hardware and operational requirements, this study lays the groundwork for designing more reliable and adaptable unmanned crop monitoring systems, thereby advancing precision agriculture globally. Conceptualization, H.Z. and N.L. ; methodology, J.X. ; software, L.C. ; validation, H.Z., N.L., and J.X. ; formal analysis, L.C. ; investigation, N.L. ; resources, S.C.; data curation, L.C. ; writing--original draft preparation, H.Z. ; writing--review and editing, H.Z. ; visualization, N.L. ; supervision, S.C.; project administration, N.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Guangdong Provincial Youth Innovation Talent Program, grant number 2021 KQNCX121, Guangzhou College of Commerce Research Project, grant number 2021 XJYB05. **Institutional Review Board Statement:** Not applicable. **Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.