A plant grower includes a base, an upper cover having a surface parallel to an upper surface of the base, the upper cover being disposed above the base so as to be spaced apart from the upper surface of the base, a plurality of growing panels disposed along the circumference of the upper surface of the base, the plurality of growing panels being rotatably disposed at the base and the upper cover, and a lighting bar extending vertically upwards from the center of the upper surface of the base to the upper cover, the lighting bar emitting light in a direction in which the plurality of growing panels is disposed, and a plurality of growing holders, into each of which a plant is inserted, disposed at one surface of each of the plurality of growing panels.

An information processing device is configured to acquire prediction information of a weather condition outside a plastic greenhouse and cultivation information of produce inside the plastic greenhouse and predict an environmental condition inside the plastic greenhouse based on the prediction information of the weather condition and the cultivation information. Cultivation information includes at least one item of information from among the type of produce, a cultivation amount, a growth state, and a cultivation ground. A prediction model may be generated with machine learning.

Systems and methods disclosed herein include a wireless horticultural system, which includes a computer, an adapter configured to receive an input from the computer, in which the input is formatted in a first native communications protocol of the computer, convert the input from the first native communications protocol into a first wireless signal, and transmit the first wireless signal to one or more controllers. The system further includes the one or more controllers, in which a first controller in the one or more controllers is connected to the adapter and configured to receive the first wireless signal from the adapter, and provide the input encoded in the first wireless signal to a device connected to the first controller.

Various implementations disclosed herein includes a method for operating lighting fixtures in horticultural applications. The method may include receiving a user input of a desired irradiance for a first color channel of one or more lighting fixtures that irradiates a plant bed, in which each of the one or more lighting fixtures comprises at least one light emitting diode (LED) array, determining, for each of the one or more lighting fixtures, a PWM setting of the first color channel such that each of the one or more lighting fixtures irradiate the plant bed at the desired irradiance based on calibration data stored in each of the one or more lighting fixtures, and applying, to each of the one or more lighting fixtures, the determined PWM setting of the first color channel.

Various implementations disclosed herein includes a method for operating lighting fixtures in horticultural applications. The method may include receiving a user input of a desired irradiance for a first color channel of one or more lighting fixtures that irradiates a plant bed, in which each of the one or more lighting fixtures comprises at least one light emitting diode (LED) array, determining, for each of the one or more lighting fixtures, a PWM setting of the first color channel such that each of the one or more lighting fixtures irradiate the plant bed at the desired irradiance based on calibration data stored in each of the one or more lighting fixtures, and applying, to each of the one or more lighting fixtures, the determined PWM setting of the first color channel.

An automatic vertical farming system may include a frame defining at least one growth area and configured to support a plurality of vertical plant growth structures within the at least one growth area. The system may include at least one light, at least one liquid conduit, and at least one gas conduit. The system may include at least one robot disposed on a top side of the frame and movably supported by the frame. The at least one robot may include at least one tool configured to manipulate the plurality of vertical plant growth structures. The system may include a control system including at least one processor configured to automatically control illumination by the at least one light, liquid flow through the at least one liquid conduit, gas flow through the at least one gas conduit, and operation of the at least one robot.

An automatic vertical farming system may include a frame defining at least one growth area and configured to support a plurality of vertical plant growth structures within the at least one growth area. The system may include at least one light, at least one liquid conduit, and at least one gas conduit. The system may include at least one robot disposed on a top side of the frame and movably supported by the frame. The at least one robot may include at least one tool configured to manipulate the plurality of vertical plant growth structures. The system may include a control system including at least one processor configured to automatically control illumination by the at least one light, liquid flow through the at least one liquid conduit, gas flow through the at least one gas conduit, and operation of the at least one robot.

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.

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.

The present invention provides a smart plant cultivation device and a smart plant cultivation system which, by using an Internet of things (IoT) technology, can automatically configure a cultivation environment according to a variety of plants, automatically perform zoning control of a light source module, and monitor and record a cultivation process of a plant through a user device.