Abstract: An exemplary computer-implemented method for administering one or more treatments to a plant in a greenhouse comprises: receiving data associated with the plant; providing the received data to a trained machine-learning prediction model to obtain: a first set of health predictions of the plant corresponding to a present time; and a second set of health predictions of the plant corresponding to a future time; determining whether the plant is unhealthy or lagging a growth trajectory based on: a first comparison based on the first set of health predictions and observed health of the plant at the present time, and a second comparison based on the second set of health predictions and a future production goal of the plant; if the plant is unhealthy or lagging the growth trajectory, identifying the treatments for the plant; and controlling one or more robots in the greenhouse to administer the treatments.

Abstract: An exemplary computer-implemented method for administering one or more treatments to a plant in a greenhouse comprises: receiving data associated with the plant; providing the received data to a trained machine-learning prediction model to obtain: a first set of health predictions of the plant corresponding to a present time; and a second set of health predictions of the plant corresponding to a future time; determining whether the plant is unhealthy or lagging a growth trajectory based on: a first comparison based on the first set of health predictions and observed health of the plant at the present time, and a second comparison based on the second set of health predictions and a future production goal of the plant; if the plant is unhealthy or lagging the growth trajectory, identifying the treatments for the plant; and controlling one or more robots in the greenhouse to administer the treatments.

Abstract: An exemplary computer-implemented method for administering one or more treatments to a plant in a greenhouse comprises: receiving data associated with the plant; providing the received data to a trained machine-learning prediction model to obtain: a first set of health predictions of the plant corresponding to a present time; and a second set of health predictions of the plant corresponding to a future time; determining whether the plant is unhealthy or lagging a growth trajectory based on: a first comparison based on the first set of health predictions and observed health of the plant at the present time, and a second comparison based on the second set of health predictions and a future production goal of the plant; if the plant is unhealthy or lagging the growth trajectory, identifying the treatments for the plant; and controlling one or more robots in the greenhouse to administer the treatments.