Technologies for low-pesticide agriculture
Five technology pathways from AI to biology that work alone or together to cut pesticide use by 50 to 90 percent in the right systems.
AI computer vision for pest, weed, and disease detection
What it does
AI-powered cameras mounted on drones, tractors, or handheld devices capture images of crops and use trained machine learning models to identify pests, weeds, or disease symptoms at early stages. Detection can happen in the field, on a mobile phone, or via satellite imagery.
How it reduces pesticide use
Early and accurate detection means farmers can act before an infestation spreads, applying targeted treatment to specific zones rather than blanket-spraying an entire field. Misidentification is a major cause of unnecessary spraying, so precise AI diagnosis directly removes wasted chemical application.
Example projects
AI-DISC, GreendaAI, CropFix, ClubAgro, Blue River Technology (part of John Deere), and research programs at ICRISAT and CIMMYT use deep learning models for field-level crop problem identification.
Best-fit use cases
Any crop with visible pest or disease symptoms. Works well in cotton, tomato, rice, maize, and vegetable crops. Most effective for farmers who currently spray on a fixed schedule without confirming actual pest presence.
Benefits
- Low entry cost via smartphone apps
- Reduces misidentification and unnecessary spraying
- Supports threshold-based action
- Accessible to smallholder farmers
Limitations
- Accuracy depends on training data quality
- Requires internet connectivity for cloud models
- Farmer adoption and habit change are the main barriers
AI spot spraying robots
What it does
Autonomous or tractor-mounted robots move through a field and use AI vision to identify weeds, pests, or diseased plants in real time. The robot sprays only at confirmed target locations, leaving the rest of the field untreated.
How it reduces pesticide use
By spraying only where a target is detected, chemical application is reduced to the minimum required dose. In weed control trials, spot spraying robots have reduced herbicide use by 50 to 90 percent compared to conventional broadcast spraying.
Example projects
Niqo Robotics (India, cotton), Blue River Technology See & Spray (U.S., row crops), Verdant Robotics (vegetables), Carbon Robotics (laser + AI).
Best-fit use cases
Large-scale vegetable, cotton, soybean, and row-crop systems where weed or pest distribution is uneven. Also suited to orchards and high-value crops where precision pays back quickly.
Benefits
- 50 to 90% herbicide or pesticide reduction in some systems
- Lower chemical cost per season
- Reduced environmental runoff
- Works around the clock with drone or robot systems
Limitations
- High initial robot cost; suited to custom hiring or FPO models for smallholders
- Requires field conditions suitable for robot navigation
- Calibration and maintenance training needed
Laser, UV-C, and non-chemical robotic control
What it does
Instead of applying any chemical, these robots eliminate weeds or surface pests using high-powered lasers, ultraviolet-C light, heat, vacuum suction, or physical mechanical action targeted at individual plants or insects.
How it reduces pesticide use
The chemical input is eliminated entirely for the targeted pest or weed. Carbon Robotics LaserWeeder, for example, destroys weed seedlings before they establish, removing the need for any pre-emergence or post-emergence herbicide on those weeds.
Example projects
Carbon Robotics LaserWeeder, TRIC Robotics (mechanical and thermal), Verdant SharpShooter (mechanical), and several university-backed UV-C pest control robot programs.
Best-fit use cases
High-value vegetable crops, organic farming operations, and any system where the cost of herbicide resistance or residue is significant. Laser weeding works best at seedling stage when weeds are small.
Benefits
- Zero chemical application for treated weeds or pests
- No herbicide resistance risk
- Suitable for organic certification contexts
- Works at night to extend operational hours
Limitations
- High capital cost
- Speed limited compared to broadcast spraying
- Currently best suited to flat terrain and wide-row crops
Biological and behaviour-based pest control
What it does
Biological control uses natural enemies, microbial agents, pheromones, and insect behaviour systems to disrupt pest cycles without chemicals. Pheromone traps confuse mating signals. Beneficial insects prey on crop pests. Microbial products kill specific insects while leaving others unharmed.
How it reduces pesticide use
When pest populations are managed through natural or biological systems, chemical interventions become the last resort rather than the default. Systems like pheromone mating disruption can suppress an entire pest generation without any insecticide spray.
Example projects
AI-GENIX SmartRavager (behaviour and pheromone system), BraveHawk (light-based insect disruption), eBionic (ecosystem-aware biological control), and established products like Trichogramma cards, BT-based biopesticides, and NPV-based sprays.
Best-fit use cases
Particularly effective against moth pests (Helicoverpa, fall armyworm), thrips, whitefly, and fruit borers. Works well in crops like cotton, tomato, chilli, rice, and fruit orchards. Can be integrated into any IPM programme.
Benefits
- Safe for pollinators and beneficial insects when correctly applied
- No residue concerns
- Builds long-term ecosystem health in fields
- Low cost per unit for established products
Limitations
- Effectiveness depends on pest species and population level
- Requires correct timing and handling
- Works best as part of an integrated system, not standalone
Digital twins and crop health decision systems
What it does
A digital twin of a farm or field integrates real-time sensor data, weather forecasts, crop growth models, pest monitoring records, and historical farm data to generate a continuously updated picture of field health and risk.
How it reduces pesticide use
Decision systems that combine multiple data streams can recommend the minimum intervention needed at the right time. Instead of spraying because it is Tuesday, farmers spray only when pest threshold, weather conditions, and crop stage all confirm that action is necessary.
Example projects
One Crop Health digital twin research programme, various ICAR digital advisory systems, and commercial platforms integrating weather, satellite imagery, and AI pest alerts.
Best-fit use cases
FPOs and large farm clusters where coordinated monitoring across multiple fields makes the data model more accurate. Also valuable for crop insurance, seed companies, and government advisory programmes.
Benefits
- Whole-season pesticide optimisation across a farm
- Supports threshold-based and risk-based decisions
- Connects AI monitoring, biological inputs, and chemical timing
Limitations
- Requires consistent data input over multiple seasons to improve accuracy
- Internet and sensor infrastructure needed
- High model complexity may require agronomist support for interpretation
Technology pathways at a glance
Compare all five pathways by what they do, their pesticide reduction potential, and leading example projects.
| Pathway | What it does | Pesticide Reduction | Example Projects |
|---|---|---|---|
| AI Spot Spraying Robots | Detects weeds or pests and sprays only the target spots, skipping healthy areas. | 50-60% in Indian pilots; up to 90% in some trials | Niqo Robotics, Blue River, Verdant Robotics, Carbon Robotics |
| Non-Chemical Robotic Control | Uses laser, UV-C, vacuum, or mechanical weeding instead of chemicals entirely. | Up to 70% in some deployments | Carbon Robotics LaserWeeder, TRIC Robotics, Verdant SharpShooter |
| Behaviour-Based Biological Control | Uses light, sound, pheromones, and insect behaviour to control pests without chemicals. | Up to 70% lower crop protection cost claimed | AI-GENIX SmartRavager, BraveHawk, eBionic |
| AI Diagnosis Apps | Diagnoses crop problems from photos and provides targeted advice before any spray. | Helps reduce unnecessary spraying; depends on adoption | AI-DISC, GreendaAI, CropFix, ClubAgro |
| Ecosystem-Based Crop Health | Combines AI, robots, biological controls, and healthy field ecosystems for whole-system pest reduction. | 50%+ system-level reduction target | One Crop Health and similar research programs |