BACKGROUND

Data efficiency arises because of two salient features of the food system: (1) high variability and diversity in terms of crop traits, environmental conditions, multi-faceted quality measures, and consumer preferences; (2) high cost—in terms of both labor and time (e.g., the innate growth cycle of crops)—associated with data collection and the low quality of observational data (e.g., self-reported dietary intake data). The first challenge gives rise to a highly complex learning space that all AI solutions need to navigate through: high dimensional input and output, reward and loss as feedback for adaptation and learning are difficult to define (e.g., the taste of a strawberry variety), and highly nonlinear and non-convex objective function landscapes. Compounding this difficult learning task is the second challenge that starves the AI models with few, noisy, and incomplete data points to learn from. These two challenges highlight the importance of data efficiency in developing effective AI algorithms in next generation food systems.

GOALS

• Identify key challenges in food systems using domain-specific examples in molecular breeding, agricultural production, food processing, nutrition.
• Develop active-learning-based approaches to address data efficiency in food systems.
• Develop sim-to-real approaches that leverage simulations to improve data efficiency in machine learning algorithms with a small amount of real-world data in food systems.

IMPACT

• Benefit multiple food system applications using AI-based solutions.