AI-powered pest monitoring and control platform for moth pest management

Overview

This solution combines solar-powered monitoring devices with artificial intelligence to improve pest control in orchards affected by moth species such as Cydia pomonella, Lobesia botrana, and Proeulia. The platform integrates data from cameras monitoring pheromone traps and tree sprouts with temperature and humidity sensors to build a detailed understanding of pest phenological cycles. By applying established and emerging entomological models to this local data, the system generates differentiated pesticide application recommendations tailored to each grower's conditions.

Validation studies with three Chilean institutions demonstrated measurable efficiency gains. Walnut producers in northern Chile participating in the Cydia pomonella study achieved historic results with no pest-related damage for the first time. The platform addresses the labor-intensive and often inaccurate nature of manual trap monitoring, helping farmers optimize treatment timing, reduce unnecessary pesticide use, and minimize environmental impact.

Technical specifications

Key features:

• Solar-powered field devices with cameras that photograph pheromone traps and tree development stages
• Integrated temperature and humidity sensors for local climate data collection
• AI and machine learning models that analyze pest population dynamics and tree phenology
• Phenological models for multiple moth species including Cydia pomonella, Lobesia botrana, and Proeulia
• User platform delivering species-specific recommendations for product selection and application timing
• Support for differentiated control objectives such as egg or larvae targeting
• Continuous remote monitoring that eliminates routine manual trap inspections

How it works: Each monitoring device is installed on a tree alongside a pheromone trap. Cameras capture images of trap captures and tree sprout development, while sensors record local temperature and humidity. The platform uses this data to estimate the cycle of the moth's next generation and determine optimal application dates for specific control products.

Technology readiness level

The platform has been validated through studies with three institutions in Chile: Universidad de Chile (Proeulia), Universidad de Talca (Lobesia botrana), and Instituto Nacional de Investigación Agropecuaria (Cydia pomonella). The Cydia pomonella study produced the first documented efficiency results, with participating walnut producers achieving pest-free outcomes for the first time. Future validation will expand monitoring across multiple devices per species to refine cycle predictions and validate population control outcomes, supervised by entomologists with species-specific expertise. The technology is commercially operational and expanding into international markets including the United States.