	US 12,250,910 B2
	Management device for cultivation of fruit vegetable plants and fruit trees, learning device, management method for cultivation of fruit vegetable plants and fruit trees, learning model generation method, management program for cultivation of fruit vegetable plants and fruit trees, and learning model generation program
Shinichi Hosomi, Kyoto (JP); Sho Sasaki, Kyoto (JP); Atsushi Hashimoto, Tokyo (JP); and Hiroyuki Miyaura, Tokyo (JP)
Assigned to OMRON CORPORATION, Kyoto (JP)
Appl. No. 17/298,729
Filed by OMRON Corporation, Kyoto (JP)
PCT Filed Sep. 29, 2020, PCT No. PCT/JP2020/036846 § 371(c)(1), (2) Date Jun. 1, 2021, PCT Pub. No. WO2021/065891, PCT Pub. Date Apr. 8, 2021.
Claims priority of application No. 2019-184104 (JP), filed on Oct. 4, 2019.
Prior Publication US 2022/0225583 A1, Jul. 21, 2022
Int. Cl. A01G 22/05 (2018.01); G06Q 10/0631 (2023.01); G06Q 50/02 (2024.01)

CPC A01G 22/05 (2018.02) [G06Q 10/06313 (2013.01); G06Q 50/02 (2013.01)]

6 Claims

1. A cultivation and management system for fruit vegetable plants and fruit trees comprising:

a greenhouse configured to house a fruit vegetable plant or a fruit tree to be cultivated;

at least one environment sensor configured to measure a value corresponding to an environment state of the greenhouse;

at least one growth sensor configured to measure a value corresponding to a growth state of the fruit vegetable plant or the fruit tree disposed in the greenhouse;

at least one environment state controller configured to control the environment state of the greenhouse;

a cultivation and management device including (i) at least one memory configured to store computer-executable instructions and at least one processor configured to execute the computer-executable instructions stored in the at least one memory, (ii) at least one integrated circuit, or both (i) and (ii) that cause the cultivation and management device to:

acquire environment state information indicating an environment state of the fruit vegetable plant or the fruit tree disposed in the greenhouse based on the value, measured by the at least one environment sensor, corresponding to the environment state of the greenhouse;

acquire growth state information indicating the growth state of the fruit vegetable plant or the fruit tree disposed in the greenhouse based on the value, measured by the at least one growth sensor, corresponding to the growth state of the fruit vegetable plant or the fruit tree;

acquire planned cultivation evaluation index information on a preplanned cultivation evaluation index of the fruit vegetable plant or the fruit tree;

implement a calculator configured to determine and output a work including a shape change work for the fruit vegetable plant or the fruit tree and being quantified according to a work type with respect to inputs of the environment state information, the growth state information, and the planned cultivation evaluation index information, the calculator using a learning model trained on a cultivation evaluation index of the fruit vegetable plant or the fruit tree cultivated, an environment state of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree, a growth state of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree, and a chronologically identified work history of a quantified historical work for the fruit vegetable plant or the fruit tree, the quantified historical work including the shape change work for changing a shape of the fruit vegetable plant or the fruit tree when cultivating the fruit vegetable plant or the fruit tree and being quantified according to the work type; and

implement an outputter configured to output the work including the shape change work for the fruit vegetable plant or the fruit tree and being quantified according to the work type,

wherein the calculator is further configured to use the learning model to determine and output a state control instruction with respect to the inputs of the environment state information, the growth state information, and the planned cultivation evaluation index information,

wherein the outputter is further configured to output the state control instruction to thereby control the at least one environment state controller to thereby control the environment state of the greenhouse, and

wherein the learning model is generated by reinforcement learning using the environment state information as a state of an environment, the quantified historical work including the shape change work for the fruit vegetable plant or the fruit tree as an action on the environment, and the cultivation evaluation index as a reward.