# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 2 Materials and Methods
### Field Maize Crop Population Collection System  
By using UAVs with monocular cameras to capture 2D image sequences and create 3D models, the field corn acquisition system leverages 3D reconstruction technology with multi-view geometry, which is extensively utilized in the domains of localization, navigation, and virtual reality. Structure from motion-multi-view stereo (SFM-MVS), feature extraction, and matching are the main technologies used in the sparse and dense reconstruction stages of 3D reconstruction.  
The study's field crop population-gathering system included a desktop computer running Windows 10 (Intel Core i5-10500, 32G RAM, Gigabyte 3070 graphics card); a DJI Phantom 4 Pro, including the drone, remote controller, and 20-megapixel CMOS camera; a tape measure; and additional tools. The DJI Elf 4 Pro drone was selected based on the following considerations. The DJI Elf 4 Pro drone was chosen for this study due to its high-resolution 20 MP camera, flight stability with GPS-assisted navigation, and cost-effectiveness compared to more expensive UAVs with LiDAR or multispectral sensors. It offers sufficient accuracy for 3D reconstruction and plant height measurement while being easy to operate, making it ideal for large-scale field data collection. Unlike higher-end UAVs, the DJI Elf 4 Pro provides a good balance of performance, ease of use, and affordability, making it well-suited for field-scale phenotyping studies. Crop field point cloud data were generated using the Pix4 Dmapper 4.5.6 software for 3D reconstruction, processed using the C++ Point Cloud Library (PCL 1.8.0), and visualized using MeshLab 1.3.2 and CloudCompare 2.9.0. The Pix4 Dmapper software 4.5.6 was configured with specific parameters to ensure high-quality 3D reconstruction:  
1. Image alignment settings: The alignment settings were configured to \"High\" to ensure accurate feature detection and matching between overlapping UAV images. The \"Keypoint Image Scale\" was set to 1 (full image resolution) for improved alignment precision;
2. Reconstruction resolution: the \"Image Scale\" for dense point cloud reconstruction was set to \"Full Scale\" to utilize the original image resolution and enhance the level of detail in the resulting point cloud;
3. Point cloud densification: the \"Densification Quality\" parameter was set to \"High\" to increase the density of the point cloud, capturing fine details of maize canopy structures;
4. Noise filtering: noise filtering was enabled with the \"Aggressive\" setting to minimize irrelevant or erroneous points caused by environmental factors, such as shadows and lighting variations;
5. Ground control points (GCPs): GCPs were used to georeference the data accurately and distributed evenly across the study area. The positional accuracy of the GCPs was \(\pm 1\) cm;
6. Output coordinate system: the coordinate system was set to WGS84/UTM Zone 50 N to align the data with standard geospatial reference systems for further analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Composite Test** & **Includes Maize** & **Remarks** \\
**Group** & **Plants Number** & **Remarks** \\ \hline
1 & b, c & Same corn plant in same field \\
2 & d, e & Same corn plant in same field \\
3 & a, b, c & Different maize plants in the same field \\
4 & a, b, c, d, e & Different plants in different fields \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composite group.  
These parameter configurations were carefully selected to balance processing efficiency and reconstruction accuracy, ensuring the generation of high-quality point cloud data for field-scale maize population analysis.