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Site-specific weed control (SSWC) can provide considerable reductions in weedcontrol costs and herbicide usage. Despite the promise of machine vision forSSWC systems and the importance of ground speed in weed control efficacy, therehas been little investigation of the role of ground speed and cameracharacteristics on weed detection performance. Here, we compare the performanceof four camera-software combinations using the open-source OpenWeedLocatorplatform - (1) default settings on a Raspberry Pi HQ camera, (2) optimisedsoftware settings on a HQ camera, (3) optimised software settings on theRaspberry Pi v2 camera, and (4) a global shutter Arducam AR0234 camera - atspeeds 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 fortesting under fallow conditions using tillage radish (Raphanus sativus) andforage oats (Avena sativa) as representative broadleaf and grass weeds,respectively. ARD demonstrated the highest recall among camera systems, with upto 95.7% of weeds detected at 5 km h$^{-1}$ and 85.7% at 30 km h$^{-1}$. HQ1and 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 radishand forage oats, respectively. Detection of the grassy forage oats was worse(P<0.05) than the broadleaved tillage radish for all cameras. Despite thevariations in recall, HQ1, HQ2, and V2 maintained near-perfect precision at alltested speeds. The variable effect of ground speed and camera system ondetection performance of grass and broadleaf weeds, indicates that carefulhardware and software considerations must be made when developing SSWC systems.

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