Investigating image-based fallow weed detection performance on Raphanus sativus and Avena sativa at speeds up to 30 km h$^{-1}$

Site-specific weed control (SSWC) can provide considerable reductions in weed control costs and herbicide usage. Despite the promise of machine vision for SSWC systems and the importance of ground speed in weed control efficacy, there has been little investigation of the role of ground speed and camera characteristics on weed detection performance. Here, we compare the performance of four camera-software combinations using the open-source OpenWeedLocator platform - (1) default settings on a Raspberry Pi HQ camera, (2) optimised software settings on a HQ camera, (3) optimised software settings on the Raspberry Pi v2 camera, and (4) a global shutter Arducam AR0234 camera - at speeds ranging from 5 km h$^{-1}$ to 30 km h$^{-1}$. A combined excess green (ExG) and hue, saturation, value (HSV) thresholding algorithm was used for testing under fallow conditions using tillage radish (Raphanus sativus) and forage oats (Avena sativa) as representative broadleaf and grass weeds, respectively. ARD demonstrated the highest recall among camera systems, with up to 95.7% of weeds detected at 5 km h$^{-1}$ and 85.7% at 30 km h$^{-1}$. HQ1 and V2 cameras had the lowest recall of 31.1% and 26.0% at 30 km h$^{-1}$, respectively. All cameras experienced a decrease in recall as speed increased. The highest rate of decrease was observed for HQ1 with 1.12% and 0.90% reductions in recall for every km h$^{-1}$ increase in speed for tillage radish and forage oats, respectively. Detection of the grassy forage oats was worse (P<0.05) than the broadleaved tillage radish for all cameras. Despite the variations in recall, HQ1, HQ2, and V2 maintained near-perfect precision at all tested speeds. The variable effect of ground speed and camera system on detection performance of grass and broadleaf weeds, indicates that careful hardware and software considerations must be made when developing SSWC systems.