Abstract: Hardware and computational systems for measuring plant weight in hydroponic grow systems. The combined weight of hydroponic grow modules that grow plants using a small amount of water covering the roots is accessible via automated robotic systems. The individual weights of grow infrastructure, plant mass, and available water are convolved. The amount of water in a growing tray can be estimated separately by slightly tipping the module, allowing water to move to one side, and measuring the weight at each of the corners of the module. After controlling for unevenness of the surface where the module is held, a machine learning model predicts the amount of water in a grow module and, subsequently, the plant mass. Reliable estimation of plant mass and water volume allows for both maintenance of precise amounts of water in growing trays and estimation of harvestable product in the grow space.

Abstract: A mobile robot configured for integration with one or more grow space components within a grow space that includes one or more grow modules, the mobile robot including: one or more sensors; a mobility mechanism; a processor; a memory; the mobile robot configured to: determine, by the mobile robot, that a grow space operation should be performed in the grow space; and based on the determination, perform, by the mobile robot, the grow space operation by integrating with at least one other component in the grow space.

Abstract: Systems and methods for high spatial and temporal resolution of environmental observations. Improved resolution is achieved by using machine learning methods to build a function from observations from frequently measuring stationary sensors to another sensor in a different location at a corresponding time. Given values from the reference sensor, the learned function can impute sensor measurements in unobserved locations and times.

Abstract: Embodiments of the disclosure involve training machine learned models using DNA-encoded library experimental data outputs and for deploying the trained machine learned models for conducting a virtual compound screen, for performing a hit selection and analysis, or for predicting binding affinities between compounds and targets. Machine learned models are trained using one or more augmentations that selectively expand molecular representations of a training dataset. Furthermore, machine learned models are trained to account for confounding covariates, thereby improving the machine learned models' abilities to conduct a virtual screen, perform a hit selection, and to predict binding affinities.