Methods and compositions for governing phenotypic outcomes in plants

One or more non-transitory computer storage media encoded with computer program instructions that when executed by a plurality of computers cause the plurality of computers to perform operations comprising:

• obtaining a model input comprising time series data characterizing a plant over a sequence of multiple previous time points, wherein the time series data comprises, for each previous time point in the sequence of multiple previous time points, at least one of:
• i) multi-omics data corresponding to the plant at the previous time point, or
• ii) phenotypic data corresponding to the plant at the previous time point;
• and
• processing the model input using a machine learning model to generate a model output that comprises, for each future time point of one or more of future time points, a prediction of at least one of:
• i) predicted phenotypic data corresponding to the plant at the future time point, or
• ii) predicted multi-omics data corresponding to the plant at the future time point;
• wherein the machine learning model has been configured through training by a machine learning training technique to:
• receive a training model input corresponding to a training plant, and
• process the training model input to generate a prediction corresponding to the training plant at each of one or more future time points;
• and
• processing the model output, generated by the machine learning model based on the time series data characterizing the plant over the sequence of multiple time points, to select one or more interventions to be applied to the plant.

A system comprising:

• one or more computers;
• and
• one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
• obtaining a model input comprising time series data characterizing a plant over a sequence of multiple previous time points, wherein the time series data comprises, for each previous time point in the sequence of multiple previous time points, at least one of:
• i) multi-omics data corresponding to the plant at the previous time point, or
• ii) phenotypic data corresponding to the plant at the previous time point;
• and
• processing the model input using a machine learning model to generate a model output that comprises, for each future time point of one or more of future time points, a prediction of at least one of:
• i) predicted phenotypic data corresponding to the plant at the future time point, or
• ii) predicted multi-omics data corresponding to the plant at the future time point;
• wherein the machine learning model has been configured through training by a machine learning training technique to:
• receive a training model input corresponding to a training plant, and
• process the training model input to generate a prediction corresponding to the training plant at each of one or more future time points;
• and
• processing the model output, generated by the machine learning model based on the time series data characterizing the plant over the sequence of multiple time points, to select one or more interventions to be applied to the plant.