Nanobot Crop Guardian Swarm

Swarm Controls

Bot count 60

Chemotaxis strength 0.70

Payload capacity (doses) 3

Biomarker diffusion rate D 0.80

Field Status

Infected remaining

Treated (total)

Bots dosing now

Sim time

0.0 s

Legend

Healthy plant

Infected plant (emits biomarker)

Treated plant

Nanobot — searching

Nanobot — dosing

Nanobot — returning to depot

How it works

Infected plants continuously leak a disease biomarker (a volatile organic compound / stress metabolite). It spreads across the field by diffusion, following Fick's second law with decay:

∂C/∂t = D·∇²C − k·C + S(x,t)

D — diffusion rate (slider)
k — fixed decay rate
S — source term, active at each infected plant

The field is solved on a 40×40 grid with an explicit finite-difference (FTCS) stencil each simulation step, clamped to a stable time-step.

Each nanobot behaves like a chemotactic bacterium: it samples the local gradient ∇C at its position and biases its heading toward it, blended with a random-walk "tumble" component:

v = normalize( χ·∇C + (1−χ)·r̂ )·speed

χ — chemotaxis strength (slider), r̂ — random unit vector

Searching (cyan) — following the gradient uphill toward the strongest plume.
Dosing (yellow) — arrived within range of an infected plant and is dwelling to apply a precision dose, which clears the local source.
Returning (purple) — payload exhausted; heading straight to the depot to recharge before rejoining the search.

This mirrors real precision-agriculture proposals for nanoscale/microscale robotic agents: cheap local sensing plus a chemical gradient replaces GPS or central coordination, so the swarm converges on outbreaks without knowing the field layout in advance.

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