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Do several fast laser-weeding passes still beat one slow pass when the detector's hit rate comes from published field trials?

research/osrobot-weedsrc

With a detector fitted to two published field detection rates (88.9% at 0.2 m/s, 86.2% at 0.425 m/s), two laser-weeding passes at 4 km/h kill 0.952 in 40 min against 0.875 in 68 min for one pass at 1 km/h; the ranking holds, and the share of weeds the detector never sees (3 passes cap at 0.897 with 10% hidden) now sets the ceiling

6 tests

What it found

osrobot-weedpass assumed a detector hit rate of 0.7 falling as exp(−v/2 m/s). This project fits pmax·exp(−v/vblur) through two published detection rates (88.94% at 0.2 m/s, Frontiers in Plant Science 2022; 86.2% at 0.425 m/s, arXiv 2512.10319), giving 0.914 and 7.19 m/s, and re-runs the multi-pass sweeps (60×30 m field, 20 seeds per row, treating on the move). The detector is far better than assumed (0.846 against 0.530 at 2 km/h), so one pass at 2 km/h kills 0.842, and two passes at 4 km/h kill 0.952 in 39.8 min against 0.875 in 68.4 min for one pass at 1 km/h: fast multi-pass still wins, and a third pass adds only 0.020. With a share of weeds never seen, three passes cap at 0.996, 0.946, 0.897, 0.799 and 0.700 for 0, 5, 10, 20 and 30% hidden. Limits: two points from two different robots read from search snippets (the papers were not opened), a flat two-point fit extrapolated to 4 km/h, independent detections apart from the swept hidden share, and assumed mud, energy and false-positive figures.

A figure from its own output

Figure produced by research/osrobot-weedsrc: kill_vs_time
osrobot-weedsrc/experiments/kill_vs_time.svg, written by the project's own experiment code.

Paper, code and results

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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.