KR102827284B1 - A smart farm control system supporting decision based on cloud - Google Patents

Abstract

The present invention relates to a cloud-based crop-specific decision-making support smart farm complex control system that automatically controls facilities and equipment in a farm using optimal control information based on decision-making by receiving crop growth information and environmental information of a smart farm cultivation area, inputting them into a pre-learned artificial intelligence algorithm to determine the current bio-index of the crop, and then generating an expected bio-index and control information for reaching the expected bio-index if it is not within a critical range.
The present invention relates to a smart farm cultivation method that receives crop cultivation information of a cultivation area and automatically controls the facilities and environment of the cultivation area through decision-making to cultivate crops, the method comprising: a first step of collecting crop cultivation information including environmental information of the cultivation area from a composite controller (10) and transmitting the collected information to a server (20) at set time intervals; a second step of extracting facility environmental information and soil (medium) environmental information from the crop cultivation information received at set time intervals, calculating an average value for each independent variable of each environmental information, and inputting the extracted information into a pre-learned artificial intelligence algorithm to determine the current bio-index of the corresponding crop; a third step of determining whether the determined current bio-index falls within a critical range of a set reference bio-index and outputting whether or not control is required; a fourth step of generating an expected bio-index and control information for controlling the facilities and environment to reach the expected bio-index if the current bio-index is out of the critical range in the third step; a fifth step of transmitting the generated control information and the expected bio-index information to the composite controller (10) installed in the cultivation area; The above-mentioned complex controller (10) comprises a sixth step in which the complex controller (10) automatically controls the facilities and environmental control devices of the cultivation field according to the received control information; a seventh step in which the complex controller (10) transmits the changed environmental information to the server (20) at set time intervals after controlling the facilities and environment of the cultivation field; an eighth step in which the server (20) calculates an average value for each independent variable from the received changed environmental information and then inputs it again into an artificial intelligence algorithm to re-judge the current bio-index in the control state and then determines whether it has reached the expected value corresponding to the expected bio-index; a ninth step in which, if the current bio-index in the control state has reached the expected bio-index as a result of the judgment in the eighth step, control stop information is generated and, if the current bio-index in the control state has not reached the expected bio-index, additional control information is transmitted to the complex controller (10); a tenth step in which the complex controller (10) automatically controls the facilities and environment of the cultivation field according to the control stop information or additional control information transmitted in the ninth step.

Description

{A smart farm control system supporting decision based on cloud}

The present invention relates to a cloud-based crop-specific decision-making support smart farm complex control system that automatically controls facilities and equipment in a farm using optimal control information based on decision-making by receiving crop growth information and environmental information of a smart farm cultivation area, inputting them into a pre-learned artificial intelligence algorithm to determine the current bio-index of the crop, and then generating an expected bio-index and control information for reaching the expected bio-index if it is not within a critical range.

As agricultural technology continues to advance for mass production of agricultural products, precision agriculture for high-quality crops is emerging.

Precision agriculture, which refers to customized farming optimized for the surrounding environment by utilizing ICT technology, collects information on factors affecting crop cultivation and precisely analyzes it to minimize unnecessary agricultural materials and work, while optimizing the efficiency of crop production management.

Precision agriculture is required because the weather environment and regional characteristics are different for each region or farmland, and because of this, the agricultural environment changes significantly from season to season. For example, the citrus industry, which occupies more than 40% of the Jeju area and accounts for more than 10% of the region's total production, is vulnerable to climate change, and because each Jeju region has different climate characteristics, it is necessary to collect regional data and analyze data for analyzing the citrus growth status according to climate change.

A representative example of precision agriculture is smart farms. The domestic smart farm market size is approximately 6 trillion won and is leading advanced agriculture. However, most domestic smart farms are focused on hardware, namely facilities and control, and there is a problem that facility costs and management costs are high. Specifically, the cost required to introduce automatic environmental control equipment is an average of 400,000 to 500,000 won per pyeong for a vinyl greenhouse, and the initial introduction cost of a smart farm of 2,000 pyeong is at least 1.3 billion won. In particular, smart farms using facility and automatic environmental control methods have no standard communication protocol, and they have a structure that can only be linked within a single, expensive system, which limits their scalability.

Looking at existing smart farm-related technologies, for example, in the case of the facility cultivation system of Patent Application No. 10-2016-0126771, it includes a greenhouse for cultivating the crops so as to effectively cultivate the crops, a sensor device installed inside and outside the greenhouse, an interface device for receiving a facility control reference value for controlling the greenhouse environment, an environment control device for controlling the greenhouse environment based on the received facility control reference value, and a memory for storing the received facility control reference value in time series. According to this technology, there is an advantage in that control signals for operating the system by an expert can be stored in time series and the facility cultivation system can be automatically controlled based on the stored data, but it does not overcome the existing limitation of requiring expensive facility costs to implement a smart farm, and there is a problem that it is difficult to apply to small-scale farms or open-field farms.

In addition, in the case of patent application No. 10-2021-0064742, a server platform for smart farm management is proposed. Specifically, the server platform for smart farm management includes a smart farm management server that manages a smart farm module that stores information about a smart farm and a farm demand module that stores information about users related to the smart farm; a smart farm connection module that searches for and connects the relevance between the smart farm stored in the smart farm module and the users stored in the farm demand module; and a farm content generation module that generates farm content accordingly when a match is made according to the connection of the smart farm connection module.

According to this technology, it can be applied to rural life experiences such as distribution of smart farms, as it has the effect of allowing multiple people to access and check the status of the assigned smart farm, and automatically checking the growth status of the smart farm according to a pre-determined schedule. However, there are still limitations in increasing agricultural competitiveness by making remote open-field farms into smart farms.

Most of the facility farms that grow orchards prefer cheap, simple, and easy-to-use systems and data services required for each period of agricultural activities rather than precision control systems. Considering the needs of these farms and the limitations of the aforementioned related technologies, a new service that can universally implement smart farms is required for facility farms such as vinyl houses as well as farms that grow orchards in open fields.

In particular, compared to experts, they lack knowledge in all aspects, such as lack of information on smart farms and lack of skills for smart farm cultivation. In addition, when farmers arbitrarily control facilities and environmental devices according to the growing season of each crop due to insufficient knowledge, the production efficiency of crops is poor and satisfactory income is not obtained.

Furthermore, as regional cultivation environments continue to change due to global warming, there is a need for technology that enables crop cultivation by automatically controlling facilities and environmental devices in the cultivation area through optimal decision-making while monitoring environmental information of the cultivation area, such as average temperature or seasonal temperature changes, altitude, slope, or slope direction of the cultivation area.

Publication of Patent Publication No. 10-2023-0081690 (2023.06.07. Composite Cultivation Management Decision-making System) Publication of Patent Publication No. 10-2023-0084994 (2023.06.13. Smart Farm Platform for Collecting Farm Environment Data and Providing Agricultural Information)

The present invention has been made to solve the above problems, and provides a smart farm complex control system with cloud-based crop-specific decision-making support, which receives crop growth information and environmental information from a smart farm cultivation area, calculates an average value for each independent variable, and then inputs the calculated average value into a pre-learned artificial intelligence algorithm to determine the current bio-index of the crop, and if it does not fall within the critical range, generates an expected bio-index and control information to reach the expected bio-index, and automatically controls the facilities and equipment of the cultivation area, thereby generating control information according to optimal decision-making for each crop and cultivating the crop to maximize production efficiency.

The solution of the present invention for solving the above problem is a smart farm cultivation method for receiving crop cultivation information of a cultivation area and automatically controlling the facilities and environment of the cultivation area to cultivate crops, comprising: a first step of collecting crop cultivation information including environmental information of the cultivation area in a composite controller (10) and transmitting it to a server (20) at set time intervals; a second step of extracting facility environmental information and soil (medium) environmental information from the crop cultivation information received at set time intervals, calculating an average value for each independent variable of each environmental information, and inputting the average value into a pre-learned artificial intelligence algorithm to determine the current bio-index of the corresponding crop; a third step of determining whether the determined current bio-index falls within the critical range of a set reference bio-index and outputting whether or not control is required; a fourth step of generating an expected bio-index and control information for controlling the facilities and environment to reach the expected bio-index if the current bio-index is output as being out of the critical range in the third step; a fifth step of transmitting the generated control information and the expected bio-index information to the composite controller (10) installed in the cultivation area; The above complex controller (10) comprises: a sixth step of automatically controlling the facilities and environmental control devices of the cultivation field according to the received control information; a seventh step of transmitting the changed environmental information to the server (20) at set time intervals after the complex controller (10) controls the facilities and environment of the cultivation field; an eighth step of the server (20) calculating the average value for each independent variable from the received changed environmental information and then re-inputting it into the artificial intelligence algorithm to re-judge the current bio-index in the control state and then determining whether it has reached the expected value corresponding to the expected bio-index; a ninth step of generating control stop information if the current bio-index in the control state has reached the expected bio-index as a result of the judgment in the eighth step, and transmitting additional control information to the complex controller (10) if the current bio-index in the control state has not reached the expected bio-index; In the 9th step, the integrated controller (10) automatically controls the facilities and environment of the cultivation field according to the control stop information or additional control information received; and after the 10th step, the 7th to 10th steps are repeatedly performed to determine whether the current bio-index of the control state has reached the expected bio-index, and automatically controls the facilities and environment of the cultivation field. The critical range is ±5% of the set reference bio-index, and the server (20) generates the expected bio-index and control information when the current bio-index is out of the set critical range and transmits the control information to the integrated controller (10). A cultivation manager terminal (15) connected to the integrated controller (15) of the cultivation field via a wireless communication network and receiving the control information is provided, and a server manager terminal (25) is provided to be connected to the server (20) by wireless communication, and when the current bio-index is out of the critical range of the set reference bio-index of the critical range and generates control information and transmits it to the integrated controller of the cultivation field, the server manager terminal (25) is connected to the server manager terminal (25). The control information including the biological index and the expected biological index is transmitted, and the crop cultivation information includes environmental information and crop growth information, and the environmental information is composed of facility environmental information and soil (medium) environmental information, and the facility environmental information includes air temperature, air humidity, carbon dioxide, solar radiation, wind direction, wind speed, and rainfall information as independent variables, and the soil (medium) environmental information includes soil EC, soil temperature, and soil moisture content as independent variables, and the crop growth information includes growth period information and image information of the crop captured by a camera, and the information on the plant height, number of leaves, leaf length, leaf width, stem diameter, crown diameter, growth length, stem thickness, seed head, flower width, flower length, flower height, number of flowers, number of blooms per flower cluster, number of fruits per flower cluster, and number of harvests per flower cluster of the crop is confirmed from the image information, and the growth period is classified into germination period, seedling period, growth period, early harvest period, and harvest period by crop, and according to the growth period, It is characterized by having different standard biometric indices.

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The above artificial intelligence algorithm uses either an artificial neural network or a decision tree algorithm.

According to the present invention having the above configuration, the current bio-index is determined using environmental information for each crop being grown in a smart farm cultivation area, and then control information for reaching the expected bio-index is generated to automatically control the facilities and equipment of the cultivation area, thereby determining optimal cultivation conditions for each crop, thereby improving production volume and income of crop farmers.

Figure 1 is a configuration diagram of a smart farm complex control system for cloud-based crop-specific decision support according to one embodiment of the present invention.
Figure 2 is an example of a screen displayed on a monitor after receiving and storing facility and environment information of a cultivation site according to one embodiment of the present invention on a server.
Figure 3 is an example of a facility and environmental equipment of a cultivation site according to one embodiment of the present invention.
Figure 4 is an example of a standard growing environment for roses, strawberries, and tomatoes according to the growing period according to one embodiment of the present invention.
Figure 5 is an example diagram showing a decision tree of facility environment information by growth period according to one embodiment of the present invention.
Figure 6 is an example diagram showing a decision tree of land environment information by growing period according to one embodiment of the present invention.
Figure 7 is a scatter plot graph for explaining the correlation between each independent variable of environmental information and the correlation between the independent variable and the bio-index (PIES) according to one embodiment of the present invention.
FIG. 8 and FIG. 9 are examples of decision-making result notifications of an artificial intelligence algorithm for the cultivation of melons and tomatoes according to one embodiment of the present invention.

Hereinafter, a cloud-based crop-specific decision-making support smart farm complex control system according to a preferred embodiment of the present invention will be described in detail with reference to the attached drawings.

As described above, the smart farm complex control system for decision-making support by crops based on the cloud according to the present invention is a smart farm cultivation method that receives crop cultivation information from a smart farm farmer's cultivation site and automatically controls the facilities and environment of the cultivation site to cultivate crops, and automatically controls the facilities and environment devices through optimal decision-making to increase crop production and farm income and minimize required cultivation costs.

The smart farm cultivation system is equipped with a complex controller (10) installed in the cultivation area of a smart farm to control facilities and environmental devices, and is connected to a server (20) installed in a remote location in advance via a wireless communication network.

The facilities and environmental devices of the smart farm cultivation area, as shown in the attached Figure 3, include skylights, side windows, and curtains as facilities, and environmental devices include irrigation, sprayers, carbon dioxide, air conditioners, boilers, ventilators, fans, valves, etc.

In addition, various sensors including temperature and humidity sensors are installed indoors to detect environmental conditions in the cultivation area, sensors are installed to sense the temperature and moisture of the indoor soil, and various sensors including temperature and humidity sensors and wind volume and wind speed sensors are installed outdoors.

When the cultivation site is a greenhouse, etc., the sensors installed inside the greenhouse are named as facility environment information sensing units and soil environment information sensing units, and the sensors installed outdoors are named as outdoor environment information sensing units.

Specifically, the facility environment information sensing unit may include a temperature sensor, a humidity sensor, and a carbon dioxide concentration detection sensor, and the soil environment information sensing unit may include a soil temperature sensor, a soil humidity sensor, and a soil EC detection sensor.

In addition, the outdoor environment information sensing unit may include a temperature sensor, a humidity sensor, a wind direction sensor, a wind speed sensor, an irradiance detection sensor, and a rainfall sensor.

All detection information sensed by the facility environment information sensing unit, soil environment information sensing unit, and outdoor environment information sensing unit is transmitted to and collated by the complex controller installed in the relevant cultivation area.

The composite controller (10) performs control operations of the facilities and environmental devices of the cultivation area, receives detection information from a sensing unit installed in the cultivation area, compiles the information, and transmits it to the server (20).

Data transmitted to the server is organized as shown in Fig. 2 as an example and can be checked through a monitor.

The server (20) is installed in a remote location, has an artificial intelligence algorithm installed, receives crop cultivation information including environmental information and growth information from the complex controller (10), determines the current bio-index, and determines whether automatic control is required by comparing it with the reference bio-index.

The above complex controller (10) and server (20) are equipped with a cultivation manager terminal (15) and a server manager terminal (25) to be described later, respectively, to receive and monitor information through a communication network, and a dedicated app (application) is installed to receive and monitor information.

The smart farm cultivation method through decision-making in the present invention receives crop growth information and environmental information of a cultivation area, inputs them into a pre-learned artificial intelligence algorithm to determine the current bio-index of the crop, and if it is not within the critical range, generates an expected bio-index and control information to reach the expected bio-index, thereby automatically controlling the facilities and equipment of the cultivation area based on optimal decision-making by artificial intelligence to cultivate crops.

Specifically, crops are grown by generating optimal control information through decision-making through a total of 10 steps.

Step 1: The complex controller (10) of the smart farm cultivation area collects crop cultivation information including environmental information and growth information of the current cultivation area based on the sensing information from various sensing units and transmits it to the server (20) at set time intervals.

Step 2: The server (20) extracts facility environment information and soil (medium) environment information from crop cultivation information received at set time intervals and calculates an average value for each independent variable of each environmental information.

Afterwards, the calculated average values for each independent variable are input into a pre-learned artificial intelligence algorithm to determine the current bio-index of the crop.

As independent variables of environmental information, independent variables essential for crop growth can be composed of atmospheric temperature, atmospheric humidity, carbon dioxide, and root zone EC, as shown in Figures 8 and 9.

Specifically, crop cultivation information includes environmental information and crop growth information, and the environmental information is composed of facility environmental information and soil (medium) environmental information.

The above facility environmental information includes air temperature, air humidity, carbon dioxide, solar irradiance, wind direction, wind speed, and rainfall information as independent variables.

In addition, the soil (medium) environmental information includes soil EC, soil temperature, and soil moisture content as independent variables.

Crop growth information includes growth period information and image information of the crop captured by a camera, and from the image information, the crop's plant height, number of leaves, leaf length, leaf width, stem diameter, crown diameter, growth length, stem thickness, particle diameter, flower cluster width, flower cluster length, flower cluster height, number of flowers, number of blooms per flower cluster, number of fruits per flower cluster, and number of harvests per flower cluster can be confirmed.

Additionally, by installing an odor sensor as crop growth information, the odor of the crop can be detected and odor information can be transmitted.

The above growing period can be classified into germination period, seedling period, growing period, early harvest period, and harvest period for each crop.

In the present invention, the current bio-index is determined by using each independent variable of environmental information as data for determining the current bio-index.

In order to determine the biological index, nine environmental information variables (carbon dioxide, medium pH, irradiance, root zone temperature, root zone moisture, air temperature, air humidity, root zone EC, medium EC) were tested to confirm the correlation with the biological index (PIES) and the biological index was determined based on the test data in the artificial intelligence algorithm.

A scatter plot graph for correlation is shown in Figure 8. As shown in Figure 8, the correlation between each independent variable and the correlation between the independent variable and the dependent variable, the biometric index, can be confirmed.

Therefore, when the average value is input for each independent variable using correlation data between independent variables or between independent variables and bio-index, the current bio-index is determined by making a decision with reference to the correlation, and when generating control information to reach the expected bio-index, it is possible to determine whether to operate each independent variable.

Step 3: Determine whether the current biometric index determined by the artificial intelligence algorithm falls within the critical range of the established reference biometric index, and determine whether or not to generate control information based on whether it falls within the critical range.

Step 4: If the current bio-index is found to be out of the critical range in Step 3, an expected bio-index and control information for controlling the facility and environment to reach the expected bio-index are generated.

The expected bio-index is the bio-index that should be achieved for each crop during the growing period using hundreds of thousands of data, and is the target bio-index that is to be achieved through automatic control of facilities and environmental devices in the cultivation area.

Control information includes facility control information and environmental control information.

For example, as shown in FIGS. 8 and 9, the current bio-index is determined based on the environmental information received from the complex controller, including essential independent variables (air temperature, air humidity, carbon dioxide, and root zone EC) and growth information including the current crop growth period, and the expected bio-index according to the growth period is output compared to the current bio-index.

In order to reach the expected bio-index, control information (facility control information, environmental control information) is generated to control the facility and environmental devices, and the control information generated in this way is transmitted to a composite controller so that the facility and environmental devices of the cultivation site are automatically controlled by the composite controller, thereby reaching the target expected bio-index.

Step 5: The server outputs the results of the decision-making model as shown in Figures 8 and 9, and transmits the generated control information and expected bio-index information to the composite controller (10) installed in the cultivation area.

Step 6: The complex controller (10) automatically controls the facilities and environmental control devices of the cultivation area according to the received control information.

For example, as shown in Fig. 3, a composite controller can control facilities (skylights, measuring devices) and operate environmental devices (carbon dioxide generators, air conditioners, lighting fixtures, air circulation fans, exhaust fans) based on control information received from a server.

As an example, in the case where the result of the decision-making model is as shown in Fig. 8, if the current bio-index is 13 and the expected bio-index is 16, control information is transmitted to operate some of the facilities and environmental devices to reach the expected bio-index.

In addition, when the current bio-index determined as in Fig. 10 is high at 16 and the expected bio-index is 14, control information is transmitted to stop the operation of all facilities and environmental devices except sunlight.

Step 7: The composite controller (10) controls the facilities and environmental devices of the cultivation area according to the control information received, and after the control, it collects the detection information received from the sensing unit and transmits the changed environmental information to the server (20) at set time intervals.

Step 8: The server (20) calculates an average value for each independent variable from the changed environmental information received, then inputs it again into the artificial intelligence algorithm to re-evaluate the current bio-index in the control state (state in which the facility and environmental devices have gone through a control process according to the control information), and then determines whether the expected value corresponding to the expected bio-index has been reached.

Step 9: If the judgment result from Step 8 is that the current bio-index in the control state has reached the expected bio-index, control stop information is generated, and if the current bio-index in the control state has not reached the expected bio-index, additional control information is generated and transmitted to the composite controller (10).

Step 10: The composite controller (10) automatically controls the facilities and environmental devices of the cultivation area according to the control stop information or additional control information received in Step 9.

That is, when control stop information is received, the control operation status of the facility and environmental devices is stopped, and the cultivation control at this time is controlled according to a pre-set schedule, just like a normal smart farm control.

In addition, when additional control information is received, the relevant facilities and environmental devices are automatically controlled according to the additional control information.

Through the above 10 steps, the cultivation information including the environmental information of the cultivation site is used to determine the current bio-index, and then the expected bio-index and control information are generated through decision-making, thereby automatically controlling the facilities and environmental equipment of the cultivation site, thereby enabling optimal simat farm cultivation based on decision-making.

At this time, after going through the above 10th step, the above 7th to 10th steps are repeated to determine whether the current bio-index in the control state has reached the expected bio-index, and the facilities and environment of the cultivation site are automatically controlled. If the expected bio-index has been reached, the automatic control is stopped.

In the present invention, when examining whether the current bio-index corresponds to a critical range after being determined, the critical range can be designated as ±5% compared to the standard bio-index set for each crop, and when the current bio-index is outside the set critical range, an expected bio-index and control information are generated, and the server transmits the generated control information to the composite controller (10).

The above critical range may be specified slightly differently for each crop and may be specified differently depending on the crop's growing period.

In addition, the standard values for the environmental information of crops may be designated differently for each crop depending on the growing period, and it is desirable that the standard bio-index is also set to different values depending on the growing period of the crop.

For example, the environmental information of crops can be set to all possible settings according to the growing period as shown in Fig. 5, but can be set differently for crops that are sensitive to temperature or soil. Figs. 6 and 7 are diagrams showing examples of decision-making at one level of depth using a decision tree with environmental and soil information as data as an artificial intelligence algorithm.

Meanwhile, a cultivation farm may be equipped with a cultivation manager terminal (15) that is connected to a complex controller (15) of the cultivation area via a wireless communication network and receives control information.

The cultivation manager terminal (15) may be a separate dedicated terminal or a tablet or smartphone used by the cultivation farm manager, and can be used by downloading and executing a dedicated application.

And, the server (20) is equipped with a server management terminal (25) for wireless communication connection.

When the server (20) generates control information and transmits it to the complex controller of the relevant cultivation area because the current bio-index is outside the critical range of the set reference bio-index, the server (20) transmits control information including the current bio-index and the expected bio-index to the server manager terminal (25).

The crop cultivation information received from the server, the current and expected bio-indices determined by the artificial intelligence algorithm, and the control information are all stored in a separate DB for each farm.

In the present invention, the artificial intelligence algorithm can be constructed as an algorithm that performs machine learning by artificial intelligence. For example, one or more of the following algorithms are used: Decision Tree, K-Nearest Neighbors, SVM (Support Vector Machines), artificial neural network, K-means, and Fuzzy Logic.

In the present invention, 200,000 environmental information data were collected separately and input into an artificial intelligence algorithm to evaluate the classification performance, thereby optimizing parameters with high classification performance.

10: Composite controller 15: Cultivation manager terminal
20: Server 25: Server Manager Terminal

Claims (7)

In a smart farm cultivation method that receives crop cultivation information from a cultivation area and automatically controls the facilities and environment of the cultivation area to cultivate crops,
A first step in which crop cultivation information including environmental information of the cultivation area is collected by a complex controller (10) and transmitted to a server (20) at set time intervals;
The second step is to extract facility environment information and soil (medium) environment information from crop cultivation information received at set time intervals, calculate the average value for each independent variable of each environmental information, and input it into a pre-learned artificial intelligence algorithm to determine the current biological index of the corresponding crop;
A third step of determining whether the determined current bio-index falls within the critical range of the established reference bio-index and outputting whether control is present;
A fourth step of generating an expected bio-index and control information for controlling the facility and environment to reach the expected bio-index, if the current bio-index is output as being out of the critical range in the third step;
Step 5 of transmitting the generated control information and expected bio-index information to the composite controller (10) installed in the cultivation area;
The above complex controller (10) is a sixth step that automatically controls the facilities and environmental control devices of the cultivation area according to the received control information;
The above complex controller (10) is a 7th step of transmitting the changed environmental information to the server (20) at set time intervals after controlling the facilities and environment of the cultivation area;
The server (20) calculates an average value for each independent variable from the changed environmental information received, and then inputs it again into the artificial intelligence algorithm to re-judge the current bio-index in the control state, and then determines whether the expected value corresponding to the expected bio-index has been reached in the 8th step;
In the 8th step, if the current bio-index in the control state reaches the expected bio-index as a result of the judgment, control stop information is generated, and if the current bio-index in the control state does not reach the expected bio-index, additional control information is transmitted to the composite controller (10).
It consists of a 10th step in which the composite controller (10) automatically controls the facilities and environment of the cultivation site according to the control stop information or additional control information received in the 9th step;
After going through the above 10 steps, the above steps 7 to 10 are repeated to determine whether the current bio-index in the control state has reached the expected bio-index, and the facilities and environment of the cultivation site are automatically controlled.
The above critical range is ±5% of the set standard biometric index,
The above server (20) generates an expected bio-index and control information when the current bio-index is outside the set critical range and transmits the control information to the composite controller (10).
A cultivation manager terminal (15) is provided that is connected to a wireless communication network to receive control information from a complex controller (15) of the cultivation area, and a server manager terminal (25) is provided to be connected to the server (20) via wireless communication.
The above server (20) generates control information and transmits it to the complex controller of the relevant cultivation area when the current bio-index is outside the critical range of the set reference bio-index, and transmits control information including the current bio-index and the expected bio-index to the server manager terminal (25).
The above crop cultivation information includes environmental information and crop growth information.
The above environmental information consists of facility environmental information and soil (medium) environmental information, and the above facility environmental information includes air temperature, air humidity, carbon dioxide, solar radiation, wind direction, wind speed, and rainfall information as independent variables.
The above soil (medium) environmental information includes soil EC, soil temperature, and soil moisture content as independent variables.
The above crop growth information includes growth period information and image information of the crop captured by the camera, and from the image information, the crop's plant height, number of leaves, leaf length, leaf width, stem diameter, crown diameter, growth length, stem thickness, seed head, flower cluster width, flower cluster length, flower cluster height, number of flowers, number of blooms per flower cluster, number of fruits per flower cluster, and number of harvests per flower cluster are confirmed.
The above growing period is classified into germination period, seedling period, growing period, early harvest period, and harvest period for each crop, and a smart farm cultivation method with cloud-based crop-specific decision support characterized by having different standard bio-indices set for each growing period.
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A smart farm cultivation method for crop-specific decision support based on a cloud, characterized in that the artificial intelligence algorithm above uses one of the following algorithms: Decision Tree, K-Nearest Neighbors, SVM (Support Vector Machines), Artificial Neural Network, K-Means, and Fuzzy Logic.

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