# Corn Plant In-Row Distance Analysis Based on Unmanned Aerial Vehicle Imagery and Row-Unit Dynamics
## Materials and Methods
### Methodology for Corn Plant Detection and Spacing Estimation Based on UAV Orthomosaic  
In this study, 210 raw high-resolution RGB images of the experimental corn field were acquired, with 21 corn-sowed rows (part of the entire trial field of 0.53 acres). The aerial image dataset was acquired using a UAV (unmanned aerial vehicle) on 2 June 2021 (24 days \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Me (m)** & **Std (m)** & **Mi (\%)** & **Mu (\%)** & **QFi (\%)** & **PS (\%)** \\ \hline \(\sum\limits_{i=1}^{Ntotal}\frac{x_{i}}{N}\) & \(\sqrt{\frac{\sum_{i=1}^{N}(x_{i}-\overline{x})}{N-1}}\) & \(\frac{N_{ini}}{N}\times 100\) & \(\frac{N_{multiple}}{N}\times 100\) & \(\frac{n}{N}\times 100\) & \(\frac{5\times t}{n}\times 100\) \\ \hline \hline \end{tabular} Note: \(N\) is the total number of observations: xi is the distance between consecutive plants in the same row; and \(N_{units}\) is the number of spacings greater than 1.5 \(\times\)\(\alpha\). \(N_{multiple}\) is the number of spacings less than 0.5 \(\times\)\(\alpha\). \(n\) is the number of spacings in the expected range [\(>\)0.5 \(\times\)\(\alpha\) or \(<\)1.5 \(\times\)\(\alpha\)], while \(\alpha\) is the predefined seeder plant distance by the farmer; and \(Std^{\prime}\) is the average plant-to-plant distance deviation if considering an expected range [\(>\)0.5 \(\times\)\(\alpha\) or \(<\)1.5 \(\times\)\(\alpha\)].  
\end{table} 
Table 1: The derived parameters together with their corresponding equations. after planting) while the plants were at the V4 vegetative growth stage. The UAV was a DJI Inspire 1 outfitted with a Zenmuse X3 camera (DJI, Shenzhen, Guangdong, China). The data were gathered at an altitude of 15 m above ground. This was highlighted in the studies of [PERSON] [29] and [PERSON] [30] as adequate for low-cost drones equipped with RGB cameras for plant phenotyping purposes. Given the characteristics of the equipment used in the study, this flight altitude setup ensured sufficient resolution for detecting corn plants at the V4 growth stage. In addition, to ensure the acquisition of high-quality data, the aerial images were collected under clear skies and at low wind-speed conditions with wind gusts \(<2\) m s\({}^{-1}\), thus eliminating the possibility of uneven illumination or blurriness within images. Maintaining a constant and low drone flight speed resulted in high-quality images with clear visible details of the imaged scene. At the stage of four visible leaves (V4), corn plants are adequately developed and, thus, clearly visible from the nadir view. For the data acquisition, the UAV was set up to capture 75% frontal and 65% side lap, which led to generating high-quality orthomosaic (5795 \(\times\) 27754) with a ground sampling distance (GSD) of 0.9 cm using Pix4 DMapper (2024 Pix4D SA). A GIS software (ArcMap v10.5, ESRI, Redlands, CA, USA) was employed to accurately obtain GPS coordinates for each plant, which were manually annotated. Subsequently, the actual spacing between the plants was calculated, enabling accurate population estimates in each plot. In total, 16,720 plants were annotated within the orthomosaic, and the distances between each consecutive plant within a row were obtained. Further, the RGB orthomosaic was divided into patches of 240 \(\times\) 240 pixels. A 20% overlap between adjacent patches was applied to ensure comprehensive coverage, resulting in a total of 810 patches further used for the plant detection and distance estimation. The workflow of UAV-derived image processing is represented in Figure 2.  
In the following, we focus on three methods with different complexities and computational efficiencies for corn detection based on created orthomosaics. Comparing them reveals their advantages and disadvantages in terms of seeding procedure evaluation. The first method for plant detection is an unsupervised segmentation approach based on the Visible Atmospherically Resistant Index (VARI) map [31].