Farmers around the world face growing challenges from crop pests, diseases, and weeds that threaten food production and agricultural economic prosperity. These threats are becoming more severe and traditional methods of identifying and managing them often require specialized knowledge and expensive resources that many farmers cannot access. This project develops AI-based decision support tools, such as smartphone apps and a chatbot, that help farmers quickly identify agricultural problems in real-time. By taking a photo of a pest, disease, or weed, farmers receive instant identification and practical advice on how to manage the problem effectively. Technology works like having an expert crop advisor or extension agent in your pocket, making advanced pest management accessible to farmers everywhere, from small family farms to large agricultural operations. This collaborative effort between the United States and QUAD member countries ensures that the decision support tools work effectively across different crops, environmental conditions, and farming systems in the U.S. and beyond. This project addresses the critical challenge of accurate, real-time identification and management of agricultural pests, diseases, and weeds across a variety of global farming systems. The research develops an end-to-end machine learning-based pipeline with uncertainty quantification, conformal prediction, and federated learning. The artificial intelligence-based models can identify several thous