Athena scanner: ai-powered smartphone pest identification for agriculture

Overview

Athena Scanner is an AI-driven mobile application designed to help growers and agricultural professionals identify pest species in the field using a simple smartphone photograph. By leveraging artificial intelligence for image processing, the tool delivers fast and precise pest identification along with georeferenced location data. This enables users to monitor pest distribution patterns and track the spread of insect resistance in Bt crops (genetically modified crops that express insecticidal proteins from Bacillus thuringiensis).

The application was recognized with third place in the Brazilian Design Award 2020 for its innovative design and user-friendly interface. It was validated by a beta testing group composed of entomology researchers and growers who submitted diverse images for identification, confirming its practical usefulness in real-world agricultural settings.

Technical specifications

• AI-based image recognition capable of processing large datasets to identify pest species with speed and precision
• Smartphone-based capture allows growers to photograph pests directly in the field without specialized equipment
• Georeferenced images record the location of pest occurrence, enabling spatial mapping of pest distribution
• Resistance monitoring for Bt crops by tracking pest occurrence patterns across fields
• User-friendly interface designed for both researchers and growers, validated through beta testing with entomology specialists
• Award-winning design recognized at the Brazilian Design Award 2020 for innovation and usability

Technology readiness level

The Athena Scanner has been validated through beta testing with entomology researchers and growers, demonstrating its assertiveness in identifying pests from submitted images. Future development plans include expanding the pest species database to broaden coverage, building a network of researchers and extension agents to provide region-specific management recommendations, and developing regional advisory systems based on pest distribution mapping. The application is positioned for continued enhancement and broader deployment across agricultural regions.