Background
Crop yield prediction is of great importance to global food production. Policy makers rely on accurate predictions to make timely import and export decisions for national food security. During the last decade, scientists and engineers have made significant headway in developing and deploying tools and devices that deliver massive, yet too often raw, data streams to food system stakeholders at unprecedented spatiotemporal resolution. Concurrently, Artificial Intelligence (AI) algorithms repeatedly break benchmarks in Computer Vision applications, Natural Language Processing (NLP), and automation. Despite its significant benefits, the complex food systems face several challenges in the application and adoption of AI. Frameworks developed in other domains can be integrated with AI approaches to tackle these challenges. Performance Based Engineering (PBE) is one of these frameworks with a robust formulation for decision making under inherent uncertainty due to the complex nature of food systems.
Goals
- Evaluate the performance of existing food facilities (including greenhouses & open fields) under environmental hazards.
- Evaluate the performance enhancement methods (for increasing crop yield, e.g., use of fertilizers & maintaining proper water drainage; for controlling crop disease, e.g., chemical & biological controls, therapy, & genetic engineering) in terms of cost and resilience.
- Quantify the benefits of real-time monitoring, and automated machinery & sensors on the resilience and performance of food facilities.
All AIFS project teams are invited to provide contributions to 1) elements in the PBFE framework and to 2) agricultural terminology databases developed by our team using the following links:
Contribute to Database 1: https://forms.gle/e9s8WnAvvz12VRMR7
Contribute to Database 2: https://forms.gle/9VyfqUfUWTqGNgZz9
Impact
- Develop a food system management framework by combining PBE and AI
- Increase the efficiency, safety & resilience of agricultural production by
- improving food yield in terms of both quantity and quality
- controlling crop diseases
- decreasing resource consumption & waste
- increasing traceability
Project Team
Khalid Mosalam
Professor of Civil Engineering, UC Berkeley
Ilias Tagkopoulos
Director and Principal Investigator (PI)
Professor of Computer Science and the Genome Center, UC Davis
Tarek Zohdi
Co-PI, Co-Lead of Food Production and Distribution Cluster, Co-Lead of Engagement
Professor of Mechanical Engineering, UC Berkeley