## 3 Results and Discussion
### Effects of \(\overset{\rightharpoonup}{v}\) and \(fps\)
Figure 9: Simulated plant hill detection rates of the vision system moving at 2.4, 12.2, and 22 \(fps\) at different travel velocities. tection models were optimized, such as illustrated in previous studies [23; 45], MVS with embedded systems shall only be applicable for agricultural field operations at walking speeds. Figure 10 illustrates the detected hills per camera frame along the first 10 m traversed distance of treatments simulated at 2.4 _fps_ (Treatments 1, 4, and 7). From Figure 10, three information can be obtained: (1) absence of vertical gaps between consecutive frames; (2) horizontal overlaps among consecutive frames; and (3) detection pattern. In Figure 10a, the absence of vertical gaps at 0.1 m/s detections infers that all the hills were detected as the vision moved along the field length. The horizontal overlaps among consecutive frames also illustrate that a plant hill was captured by more than one processed frame. Finally, a detection pattern was observed to repeat every 24 consecutive frames or approximately every 1 m length. The length of the pattern was calculated by multiplying the number of frames to complete a cycle and \(d_{f}\). In contrast, the vertical gaps in some consecutive frames at 1.3 m/s, shown in Figure 10b, illustrated the missed detections. Horizontal overlaps were also absent. Hence, the detected plant hills were only represented in the frame once. The vision module traveled too fast and processed the captured frame too slowly at the set capture width, as demonstrated by the detection pattern of one missed plant hill every seven consecutive frames or approximately every 3.8 m traversed distance. Similar results were also observed at 2.5 m/s travel velocity, as shown in Figure 10c. However, the vertical gaps were more extensive than Figure 10b due to faster travel speed. Observing the detection pattern showed that 14 plant hills were being undetected by the vision system every five frames or approximately every 5.21 m traversed distance. This pattern that forms every 5.21 m further explains the difference in the \(r_{d,th}\) and \(r_{d,sim}\) in the sensitivity analysis in Section 3.1, when the traversed distance was only 1 m. A complete detection pattern was already formed when the distance was more than 10 m, resulting in better detection rate estimates. From these results, two vital insights can be drawn. First, at \(r_{o}<1\), \(r_{d,th}\) shall have a margin of error when the length of the detection pattern is less than the traversed distance. Second, concerning future studies, object tracking algorithms, such as Euclidean-distance-based tracking [46], that requires objects should be present in at least two frames, would be not applicable when \(r_{o}\leq 1\). Hence, the importance that \(r_{o}>1\) in MVS designs is further emphasized. Figure 10: Range of plant hill indices that were detected per frame along the first 10 m of the simulated field at 2.4 _fps_, 0.5 m capture width, 0.2 m hill spacing at (**a**) 0.1 m/s, (**b**) 1.3 m/s, and (**c**) 2.5 m/s. Blue broken lines enclose a detection pattern, while broken red lines specify the missed plant hills.