IRRIGATION CONTROL WITH DEEP REINFORCEMENT LEARNING AND SMART SCHEDULING

Disclosed are various embodiments for deep reinforcement learning-based irrigation control to maintain or increase crop yield and/or other desired crop status, and/or reduce water use. One or more computing devices can be configured to determine an amount of water to be applied to at least one crop...

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Bibliographische Detailangaben
Hauptverfasser:	YANG, Yanxiang, PORTER, Dana O, MAREK, Thomas H, HU, Jiang, HEFLIN, Kevin R, KONG, Hongxin
Format:	Patent
Sprache:	eng
Schlagworte:	AEROPLANES AGRICULTURE AIRCRAFT ANIMAL HUSBANDRY AVIATION CONTROL OR REGULATING SYSTEMS IN GENERAL CONTROLLING COSMONAUTICS CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPSOR SEAWEED FISHING FORESTRY FUNCTIONAL ELEMENTS OF SUCH SYSTEMS HELICOPTERS HORTICULTURE HUMAN NECESSITIES HUNTING MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS PERFORMING OPERATIONS PHYSICS REGULATING TRANSPORTING TRAPPING WATERING
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Beschreibung
Zusammenfassung:	Disclosed are various embodiments for deep reinforcement learning-based irrigation control to maintain or increase crop yield and/or other desired crop status, and/or reduce water use. One or more computing devices can be configured to determine an amount of water to be applied to at least one crop in at least one of a plurality of irrigation management zones through execution of a deep reinforcement learning routine. Further, the computing devices can determine a start time and an end time to be applied to the at least one of the plurality of irrigation management zones based at least in part on the amount of water determined by the deep reinforcement learning module. Finally, the computing devices can instruct an irrigation system to apply irrigation to the at least one of the plurality of irrigation management zones in accordance with the start time and the end time.