GORSENTAM's Predictive Model for Tuta Absoluta Control

In recent years, Tuta absoluta has become one of the most destructive pests threatening tomato production worldwide. In response to this challenge, GORSENTAM has developed and deployed a predictive and warning model that integrates real-time field data, weather-based degree-day calculations, and AI- supported pheromone trap monitoring to support tomato growers with timely and informed decisions.

The system begins when the grower inputs the tomato planting date. From that point, digital pheromone traps installed in the field monitor pest activity. Once the first adult moth is detected, the system automatically marks this as the biofix date signaling the start of active model tracking. Using weather data from nearby agricultural stations, the model then calculates pest development stages and sends timely alerts to farmers for appropriate interventions, tailored to specific biological thresholds.

Importantly, the model also considers harvest timing to prevent unnecessary pesticide applications during late-season stages, contributing to more sustainable, environmentally responsible practices. By combining field-ready hardware, machine learning, and a farmer-centric platform, GORSENTAM’s approach empowers growers to act earlier, apply treatments more precisely, and reduce overall pesticide use. This innovation not only enhances pest control efficiency, but also promotes resilience and sustainability in digital agriculture.