**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** A CORNERSTONE OF SUCCESSFUL IMPLEMENTATION OF INTEGRATED PEST MANAGEMENT (IPM) IN AGRICULTURE IS CORRECT PEST IDENTIFICATION BUT THIS REQUIRES TIME AND EXPERTISE. AS A RESULT, MANY GROWERS FORGO IDENTIFICATION AND PESTICIDE APPLICATIONS ARE MADE UNNECESSARILY. THIS IS A PROBLEM BECAUSE INDISCRIMINATE USE OF PESTICIDES IS COSTLY TO GROWERS AND HARMFUL TO HUMAN HEALTH AND THE ENVIRONMENT. MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE (AI) FOR AUTOMATED PEST IDENTIFICATION FROM PHOTOGRAPHS HAS THE POTENTIAL TO ADDRESS THIS ISSUE.THE GOAL OF THIS PROJECT IS TO DEVELOP SUCH ANAI-BASED DECISION SUPPORT SYSTEM FOR PEST IDENTIFICATION IN WHEAT-BASED PRODUCTION SYSTEMS OF THE INLAND PACIFIC NORTHWEST (PNW) USA, WITH EXTENSIBILITY TO OTHER WHEAT GROWING REGIONS. IN ADDITION TO THE IDENTIFICATION TOOL, THIS SYSTEM WILL ALLOW USERS TO SHARE PHOTOS AND OTHERINFORMATION AS WELL AS EXPERTISE ABOUT PESTS FOUND. THIS SYSTEM WILL BE AVAILABLE TO GROWERS, CROP RESEARCHERS, AND EXTENSION EDUCATORS AS A DESKTOP COMPUTER INTERFACE AS WELL AS IOS AND ANDROID APPS.