(Google/Alphabet Modeling Talk Series) Predictive Pattern Recognition of Plant Growth Traits in Simulated and Controlled Environments

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Abstract — With the growing prevalence of challenges in global food systems, including restricted access to fresh produce in remote areas and issues faced within traditional cultivation practices, outlooks on controlled environment agriculture (CEA) offer prospective solutions through precision methods of localized food production. Controlled environments within crop production systems provide opportunities to observe and cultivate plants with diverse characteristics, or phenotypes, making it suitable for specialized applications from trait selection for cultivar development and biopharming to food production in extreme and resource scarce environments. Subsequently, the ability to accurately predict and represent the phenotypic expression of a plant and its trajectory at various points in its growth cycle demonstrates a fundamental step towards advancing simulation and modeling research within CEA. In this talk we discuss the predictions and representations of plant growth patterns in simulated and controlled environments that are important for addressing various challenges in plant phenomics research and focus on the spatiotemporal modeling of plant traits and the integration of dynamic environmental interactions. We provide an examination of deterministic, probabilistic, and generative modeling approaches, emphasizing their applications in high-throughput phenotyping and simulation-based plant growth forecasting. Key topics include regressions and neural network-based representation models for the task of forecasting, limitations of existing experiment-based deterministic approaches, and the need for dynamic frameworks that incorporate uncertainty and evolving environmental feedback.

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