The Constant War Against Pests, Diseases, and Weeds (Reactive Management)

Problem Statement

Current pest and disease management is predominantly reactive—applying chemicals only after visible infestation. This leads to substantial yield loss, promotes pest resistance due to blanket spraying, increases chemical residues in produce, and raises input costs significantly. A shift to a preventative and precision approach is critical.

Innovation Opportunities

• AI- and Sensor-based Early Detection and Diagnosis Systems: Deploying autonomous field scouts, drone-based hyperspectral imaging, or IoT-enabled pheromone traps integrated with AI image recognition for real-time, hyperlocal detection of stress.
• On-field Serological and Molecular Probes: Developing simple, rapid, and affordable point-of-care diagnostic kits (similar to COVID-19 rapid tests) for farmers to identify specific viral, bacterial, or fungal pathogens in the field.
• Forecasting and Precision Control Technologies: Predictive models that combine weather data, historical disease incidence, and in-field sensor data to forecast outbreaks, enabling variable-rate, site-specific application of biologicals or minimal chemicals.

Use Case Example

A coconut farmer uses a mobile app linked to a small field camera. The AI detects early signs of Mahali disease on a few leaves, forecasts its spread based on humidity, and recommends the exact location and minimal dosage for biological control.