Abstract: An irrigation system for a vertical farming structure having vertical grow towers and associated conveyance mechanisms for moving the vertical grow towers through a controlled environment, while being exposed to controlled conditions, such as lighting, airflow, humidity and nutritional support. The present disclosure describes a grow tower conveyance system that moves vertically-oriented grow towers to select positions along a grow line. An irrigation line having apertures at the select positions provides aqueous nutrient solution to the grow towers, while a gutter structure captures excess solution. In a closed loop system, the excess solution returns to a recirculation tank.

Abstract: An irrigation system for a vertical farming structure having vertical grow towers and associated conveyance mechanisms for moving the vertical grow towers through a controlled environment, while being exposed to controlled conditions, such as lighting, airflow, humidity and nutritional support. The present disclosure describes a grow tower conveyance system that moves vertically-oriented grow towers to select positions along a grow line. An irrigation line having apertures at the select positions provides aqueous nutrient solution to the grow towers, while a gutter structure captures excess solution. In a closed loop system, the excess solution returns to a recirculation tank.

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 autonomous robot for harvesting produce from a plant include a base, an arm coupled to the base, and an end-effector coupled to the arm. The end-effector includes one or more grippers, each having a first cutter, second cutter, and a compliant member between the first cutter and the second cutter. The first cutter is configured to cut a stem of the produce at a first location. The second cutter is configured to cut the stem of the produce at a second location. The compliant member is configured to plastically deform to hold the stem of the produce.

Abstract: An autonomous robot for harvesting produce from a plant include a base, an arm coupled to the base, and an end-effector coupled to the arm. The end-effector includes one or more grippers, each having a first cutter, second cutter, and a compliant member between the first cutter and the second cutter. The first cutter is configured to cut a stem of the produce at a first location. The second cutter is configured to cut the stem of the produce at a second location. The compliant member is configured to plastically deform to hold the stem of the produce.

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 irrigation system for a vertical farming structure having vertical grow towers and associated conveyance mechanisms for moving the vertical grow towers through a controlled environment, while being exposed to controlled conditions, such as lighting, airflow, humidity and nutritional support. The present disclosure describes a grow tower conveyance system that moves vertically-oriented grow towers to select positions along a grow line. An irrigation line having apertures at the select positions provides aqueous nutrient solution to the grow towers, while a gutter structure captures excess solution. In a closed loop system, the excess solution returns to a recirculation tank.