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Is one slow weeding pass better than several fast ones, and does treating on the move change that?

research/osrobot-weedpass

A laser-weeding robot that drives three fast passes (4 km/h) kills 0.780 of the weeds in 75 min, against 0.607 in 83 min for one slow pass (1 km/h), and treating on the move instead of stopping cuts time by 6% at 0.3 weeds/m² and 39% at 3 weeds/m²; the ranking survives 30% of weeds being invisible to the detector

7 tests

What it found

osrobot-fieldreal left two next steps: a second pass over missed weeds and treating on the move. This project adds both to its sourced-constant weeding model (2 m swath, 0.73 s per weed, 99.2% kill from laser-weeding literature; 60×30 m field, 20 seeds per row). At 1 weed/m², three passes at 4 km/h kill 0.780 of the weeds in 75.1 min, against 0.607 in 82.8 min for one pass at 1 km/h; two passes at 2 km/h reach 0.778 in 90.5 min. Because the assumed detector loses hits as speed rises, the second pass recovers more than speed costs. Treating on the move rather than stopping cuts time by 6% at 0.3 weeds/m² and 39% at 3 weeds/m² (1 km/h) with identical kill, and is bounded by laser throughput at 10 weeds/m². If 30% of weeds are never seen by the detector, kill falls (three passes at 2 km/h: 0.894 to 0.627) but the pass-count ranking is unchanged. Stylised and preliminary: detector hit rate (0.7, swept 0.5 to 0.9) and blur law, false positives, mud, energy per metre and beam steering are assumed; passes are independent except for the swept hidden share; no weed-density source was found; simulation only.

A figure from its own output

Figure produced by research/osrobot-weedpass: kill_vs_time
osrobot-weedpass/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.