FACT-AI: HARNESSING ARTIFICIAL INTELLIGENCE FOR IMPLEMENTING INTEGRATED PEST MANAGEMENT IN SMALL-GRAIN PRODUCTION SYSTEMS

The long-term goal of this project is to develop and deploy an open-source, AI-based application for pest identification in wheat-based production systems of the inland Pacific Northwest (PNW) of the USA, with extensibility to other wheat growing regions.The main objectives and sub-objectives of the project are:1. Create an open-source artificial intelligence software framework for automated identification of Inland PNW cereal system crop pests from cell phone photographs1a. Collect images of current and anticipated insect pests of Inland PNW cereal crops and rotational crops and train an artificial neural network (ANN) to classify them to species1b. Refine the image processing, ANN training, and prediction for efficiency in various visual contexts and pest combinations and to utilize real-time interactions with users2. Incorporate the framework from (1) into an AI-aided decision support system (DSS) and community-based resource for managing pests in Inland PNW cereal systems.2a. Couple the identification framework with recommendations within a mobile application for use by producers and pest advisors2b. Build in the capacity for users to upload images into a web portal for community feedback and supervised inclusion in the ongoing training database3. Refine and disseminate the system developed in (2) to the target user populations3a. Solicit volunteer test users through extension outlets, grower meetings and other conduits3b. Release Version 1.0 and disseminate throughout the Inland PNW and in other regions with shared rotational crops and pest complexes