# Corn Plant In-Row Distance Analysis Based on Unmanned Aerial Vehicle Imagery and Row-Unit Dynamics
## 3 Results
### In-Row Plant Uniformity Estimators in the Context of Plant Detection
The Mask R-CNN model has the smallest variability in plant spacing (15.96%), the TM model has the highest variability (20.82%), and the VARI (19.66%) model has spacing variability that is closest to the GT (18.88%). The Interaction Between Seeding Unit Vibration and Plant Distribution Faults as a Function of Working Speed  
An analysis of seeding row unit vibration was performed to assess and quantify its effect on seeding quality. Additionally, the Mask R-CNN data and vibration parameters were analyzed in relation to working speed to validate the reliability of the machine learning method for specific applications. As seen in Figure 8, the results suggest that  
Figure 6: Comparative effectiveness of various plant detection techniques using GT data as reference values: (**a**) absolute values included and the proportion of mis-spaced plants in the overall population (**b**). Figure 7: Plant spacing uniformity indicators with relative metrics. varying seeding velocities influence the frequency of gaps in the plant population, reducing the plant population, which supports the results obtained by [PERSON] et al. [48] and [PERSON] et al. [49]. There has been a slight drop in the number of identified plants as the working speed increased. From Figure 9, it is clear that the vibration amplitude of the seeding row unit gradually increased as the working speed of the seeder reached 13 km/h. When the working speed was increased from 8 to 10 km h\({}^{-1}\), the RMS (root mean square) parameter indicated an increase in vibration energy. However, further speed increases did not significantly affect the vibration's energy. The interaction between the seeder structure and the soil layer resulted in distinct vibration characteristics at a particular working speed. When the speed reached 10 km h\({}^{-1}\), additional frequencies that could be excited dampened the main vibration components that speed could enhance. Looking forward at Figure 10, the major frequencies, as well as vibration amplitude, can be noted for specific working speeds. The most prominent features are the spectral densities within the frequency range of 0 to 7 Hz. Two more frequencies have noticeable amplitudes (12-15 Hz for 13 and 11 km h\({}^{-1}\) and 28-30 Hz for 13 km h\({}^{-1}\)). The highest vibration energy was detected at a speed of 13 km h\({}^{-1}\), followed by vibration at 12 km h\({}^{-1}\). Working speeds of 9 and 11 km h\({}^{-1}\) produced similar vibration amplitudes as well as 8 and 10 km h\({}^{-1}\). To assess the impact of the vibration of the seeding row unit on the quality of sowing parameters, as well as to evaluate the compatibility of established relationships for different methods of detecting corn plant spacing, a correlation analysis was conducted. By showing seeding parameters for specific data types (e.g., GT and Mask R-CNN) computed for a specific speed and, consequently, vibrational energy, this may indirectly reflect the discrepancy between actual and estimated spacings. The results of this analysis are presented in Figure 11. The relationship between vibration power (RMS), amplitude (peak-to-peak), and aspects of seeding quality exhibited similar characteristics. However, the amplitude  
Figure 8: The expected number of plants compared to the number of detected plants for each row along a selected route. The blue line indicates the estimated plant numbers as calculated by the Mask R-CNN model, whereas the red line depicts the theoretical plant numbers. Figure 9: The expected number of plants vs. the number of detected plants per row level. showed a somewhat better correlation with seeding quality compared to vibration energy. [PERSON] et al. [50] observed that variations in field conditions affect the nature of the vibration of seeding row units, making it challenging to anticipate potential disturbances in sowing quality caused by vibrations. Nevertheless, the employed detection methods exhibited varying degrees of association with vibrations despite their comparable nature.