Ai-powered early pest detection and decision-support system for high-value tree crops

Overview

AgriSciense is an AI-based early-warning and decision-support system for protecting high-value tree crops. It combines multiple sensing modalities to detect hidden pest activity before visible symptoms emerge, giving growers a practical window to intervene early. By shifting pest management away from blanket spraying and reactive scouting toward predictive, precision protection, the platform helps reduce tree mortality, unnecessary pesticide use, and economic losses.

The system is designed as a scalable intelligence layer that can be used across orchards, plantations, and forests. Its modular architecture also makes it adaptable for smart traps, garden monitoring, and structural pest applications, broadening the long-term commercial value beyond field crops.

Technical specifications

Core platform features:

• Combines acoustic sensing, biochemical / volatile organic compound (VOC) signals, microclimate sensors, and 360-degree visual monitoring
• Fuses multimodal sensor data using edge-AI models to assess tree health and infestation risk
• Generates tree-level early warnings and supports targeted interventions
• Uses low-power hardware with Bluetooth Low Energy (BLE) connectivity for flexible field deployment
• Supports mobile apps and dashboards for species identification, alerts, activity tracking, and device health monitoring
• Includes cloud analytics and over-the-air (OTA) update capability for remote model and software updates

Validation performance:

• Field trials have demonstrated 84% early-detection accuracy when identifying hidden pest activity.
• Architecture is modular, low-power, and designed for practical deployment in agricultural and consumer pest-monitoring settings.

Planned next steps:

• Collect labeled datasets for targeted household, structural, lawn, and garden pests in controlled and real-world conditions
• Miniaturize and adapt sensing hardware for low-cost, low-maintenance consumer deployment
• Train pest-specific classification models
• Conduct pilot deployments to validate sensitivity, identification accuracy, false-alarm rates, battery life, and ease of installation
• Work with a consumer products partner to support manufacturing, packaging, and integration

Technology readiness level

The system is currently at an advanced prototype stage. The core detection technology has been demonstrated in field trials, with strong early-detection accuracy. The next stage will involve broader pilot deployments to validate pest-specific identification, false-alarm rates, battery performance, and real-world usability. AgriSciense expects the path from validated prototype to commercial deployment to take roughly 3–5 years.

Key limitations at this stage include the need for additional labeled data for non-tree applications, hardware miniaturization for consumer-level production, and validation of long-term field performance before large-scale commercialization.