Swarm-intelligence micro-robots that continuously monitor crop fields, greenhouses, and storage facilities — detecting biological threats before they become outbreaks.
Continuous autonomous sweeps across cereal, vegetable, and fruit crops. Detects fungal spores, bacterial blight, and insect vectors at sub-threshold densities — weeks before visible symptoms appear.
Greenhouse and vertical farm deployment. Micro-robots navigate between rows, monitoring humidity micro-climates, detecting Botrytis, Pythium, and other high-value crop pathogens in real time.
Grain silos, cold stores, and packing houses. Continuous monitoring for mycotoxin-producing moulds, rodent activity, and temperature anomalies that signal spoilage risk.
Waterway and reservoir surveillance for algal blooms, Phytophthora, and waterborne pathogens that enter fields through irrigation — a primary but often overlooked infection vector.
The Bio-Robotix ground robot integrates eight tightly coupled hardware and software layers — from raw multi-spectral sensing at the crop canopy through to autonomous Cold Atmospheric Plasma (CAP) treatment and a parallel digital-twin simulation loop. Each layer feeds the next, creating a closed-loop system that detects, classifies, and neutralises biological threats without human intervention.
RGB, multispectral, and depth cameras combined with active illumination and environmental sensors (light, temperature, relative humidity, CO₂) capture a complete spectral and spatial picture of the crop canopy. This raw multi-modal data is the foundation every downstream layer depends on.
Computer vision and feature extraction pipelines convert raw imagery into 3D maps, crop counts, health estimates, and biomass measurements. Trained models detect disease lesions, pest signatures, and stress indicators at leaf level — flagging threats before they spread.
4G/5G/Wi-Fi antenna, an on-board SoC/GPU edge computer, and a telemetry-and-storage module process perception outputs locally in real time. Low-latency edge inference means treatment decisions are made in the field, not in a distant cloud.
RTK-GNSS + IMU localisation, point-cloud mapping, and global/local path planning keep the robot on precise row-by-row trajectories. Geofence and E-Stop safety controllers ensure the robot halts immediately if it encounters an obstacle or leaves its authorised zone.
A 6-DOF robotic arm with a tool-changer end effector switches between a precision CAP sprayer and a harvester tool. Dose control and force/contact sensing ensure plasma is delivered at the correct distance and concentration for effective pathogen neutralisation without crop damage.
A modular chassis with four-wheel drive, independent suspension, and steering actuators navigates uneven terrain, crop rows, and irrigation ruts at 0–6 km/h. The platform carries a 25–40 kg configurable payload across 6–8 hour missions.
Soil and terrain models, root and moisture sensors, and irrigation drip-line awareness let the robot adapt its path and treatment plan to field conditions in real time — avoiding waterlogged zones, adjusting spray dose for soil type, and logging geo-tagged environmental readings.
A parallel 3D field model running physics, fluid-spray, and plant-growth simulations continuously validates robot behaviour before deployment. A simulation-to-real feedback loop ingests telemetry logs and images from the field to update domain randomisation parameters, tightening the gap between virtual and physical performance.
KEY PLATFORM SPECIFICATIONS
The perception layer identifies the pathogen class — fungal, bacterial, viral, or pest — and maps its GPS-tagged location. Severity scoring determines whether immediate CAP treatment or monitoring is required.
The autonomy layer generates a micro-path to the affected plant. The 6-DOF arm positions the CAP nozzle at the optimal standoff distance (0.3–10 m sensing range, sub-centimetre arm precision) for targeted plasma delivery.
CAP generates reactive oxygen and nitrogen species (RONS) at ambient temperature and pressure. These species disrupt pathogen cell membranes and DNA without heat damage to the crop, leaving no chemical residue and requiring no withdrawal period.
After treatment the robot re-images the zone. The AI layer compares pre- and post-treatment spectral signatures to confirm pathogen reduction. Results are logged with GPS coordinates, timestamp, dose, and outcome for traceability.
Treatment outcomes, environmental conditions, and robot telemetry are fed back into the digital-twin simulation. The fluid-spray and plant-growth models are updated, improving dose predictions for future missions in similar field conditions.
Anonymised outcome data from all deployed robots is aggregated to refine the central AI model. Each new deployment benefits from the collective field experience of the entire Bio-Robotix fleet.
Real-time 3D field maps updated every 15 minutes. Risk zones colour-coded by threat severity and pathogen type.
Weather-integrated AI models forecast pathogen spread trajectories up to 72 hours ahead, enabling pre-emptive intervention.
Correlates detection data with historical yield models to quantify economic risk and prioritise treatment zones.
REST API and webhook support for farm management systems, ERP platforms, and agronomist dashboards.
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