System for automatically controlling hydroponic cultivation environment according to life cyclic growth state of plant for plant factory

Abstract

The present invention relates to an automatic hydroponic cultivation environment control system based on the life cycle developmental state of a plant factory type crop. According to the present invention, the automatic hydroponic cultivation environment control system based on the life cycle developmental state of a plant factory type crop comprises: a data input unit that receives crop data including a crop image capturing the current developmental state of the crop, the type of crop, and the current life cycle of the crop; a developmental state analysis unit that scores the current developmental state of the crop from the input crop data based on pre-established life cycle sample image data for each crop and classifies it into one of a plurality of grades; and a control unit that controls the nutrient solution concentration and cultivation environment conditions applied to the crop according to the classified developmental state. According to the present invention, the growth state of a crop can be estimated from a currently captured crop image based on sample images of the crop's life cycle and image processing, and the nutrient solution concentration and cultivation environment conditions can be adaptively adjusted according to the current growth state.

Description

Technology Field

[0001] The present invention relates to an automatic hydroponic cultivation environment control system based on the life cycle developmental state of a plant factory type crop, and more specifically, to an automatic hydroponic cultivation environment control system based on the life cycle developmental state of a plant factory type crop that estimates the developmental state of the crop using image sensing of a camera and automatically controls the nutrient solution concentration and cultivation environment conditions according to the developmental state of the crop. Background Technology

[0002] A plant factory refers to a system that can automatically and continuously produce crops regardless of season or location by artificially controlling environmental conditions within the facility.

[0003] Generally, since the internal space of a plant factory is isolated from the outside, it is necessary to artificially control conditions such as temperature, humidity, CO2 concentration, and nutrient solution; additionally, it is necessary to control environmental conditions, including the illuminance of LED light sources, light exposure time, and nutrient solution conditions that affect crop growth, to an optimal state.

[0004] Furthermore, the light compensation point and light saturation point of plants can vary depending on the intensity of the LED light source, and providing appropriate light intensity and wavelength is crucial for plant growth. For example, if the light intensity is low, it may not exceed the light compensation point, leading to insufficient photosynthesis and potentially slowing or even causing plant growth to fail. Conversely, if the light intensity is high, it may exceed the light saturation point, preventing further increases in photosynthesis and potentially resulting in energy waste.

[0005] Furthermore, crops vary slightly in the types and amounts of nutrients they can absorb throughout their life cycle, and there is a need to adaptively change environmental factors that must be controlled according to the conditions of the crop's leaves, stems, and size.

[0006] The technology forming the background of the present invention is disclosed in Korean Published Patent No. 10-2014-0145782 (published Dec. 24, 2014). The problem to be solved

[0007] The present invention aims to provide an automatic hydroponic cultivation environment control system based on the life cycle developmental state of a plant factory-type crop, which estimates the growth state of the crop based on image sensing of a camera and prescribes customized nutrient solution concentration and cultivation environment conditions according to the growth state of the crop. means of solving the problem

[0008] The present invention relates to an automatic hydroponic cultivation environment control system based on the life cycle developmental state of a plant factory-type crop, comprising: a data input unit that receives crop data including a crop image capturing the current developmental state of the crop, the type of crop, and the current life cycle of the crop; a developmental state analysis unit that scores the current developmental state of the crop from the input crop data based on pre-established life cycle sample image data for each crop and classifies it into one of a plurality of grades; and a control unit that controls the nutrient solution concentration and cultivation environment conditions applied to the crop according to the classified developmental state.

[0009] Additionally, the growth status analysis unit comprises: a feature extraction unit that extracts feature values ​​for multiple variables related to the growth status of the crop from the crop image; a data extraction unit that extracts target sample image data matched to the type of crop and the current life cycle from among pre-established life cycle sample images for each crop; a conversion unit that calculates a growth status score of the crop by combining individual scores converted for each variable, which are obtained by comparing the feature values ​​for each variable extracted from the target sample image data with the feature values ​​for each variable extracted from the input crop image; and a grade classification unit that classifies the growth status of the crop into one of low growth, normal growth, or abnormal growth by comparing the quantified growth status score with a plurality of pre-set threshold values.

[0010] In addition, a plurality of variables related to the growth status of the crop may include at least one of the total size of the crop, leaf color, stem thickness, presence or absence of fruit, and presence or absence of flower.

[0011] In addition, the conversion unit calculates the growth status score by applying weights to the individual scores converted for each of the plurality of variables and summing them up, and the weights may be set differently for each variable according to the importance of the variable.

[0012] In addition, the weights applied to each of the plurality of variables may be set differently depending on the type of crop.

[0013] In addition, the above-described hydroponic environment automatic control system may further include a statistics unit that provides growth status score trend data using the growth status scores of the crops analyzed by date.

[0014] In addition, the control unit can adjust the nutrient solution concentration and cultivation environment conditions by using a control action corresponding to the classified grade among a plurality of control actions.

[0015] In addition, the above-described hydroponic environment automatic control system may further include a shipment time prediction unit that predicts the shipment time of the crop by applying the type of crop, current life cycle, scored growth status score, and classified current growth status to an AI-based pre-trained prediction model.

[0016] In addition, the control unit can increase, maintain, or decrease the growth rate of the crop by controlling the nutrient solution concentration and cultivation environment conditions based on the result of comparing the predicted crop shipment time with a preset target shipment time.

[0017] In addition, a method for automatically controlling hydroponic nutrient solution concentration performed by an automatic hydroponic environment control system comprises: a step of receiving crop data including a crop image capturing the current growth state of a plant factory-type crop, the type of crop, and the current life cycle of the crop; a step of scoring the current growth state of the crop from the input crop data based on pre-established sample image data by life cycle for each crop and classifying it into one of a plurality of grades; and a step of controlling the nutrient solution concentration and cultivation environment conditions applied to the crop according to the classified growth state.

[0018] Additionally, the step of classifying the growth status of the crop comprises: a step of extracting feature values ​​for a plurality of variables related to the growth status of the crop from the crop image; a step of extracting target sample image data matched with the type of crop and the current life cycle from among pre-established sample images for each crop by life cycle; a step of calculating a growth status score of the crop by combining individual scores converted for each variable, which are obtained by comparing the feature values ​​for each variable extracted from the target sample image data with the feature values ​​for each variable extracted from the input crop image; and a step of classifying the growth status of the crop into one of low growth, normal growth, and abnormal growth by comparing the quantified growth status score with a plurality of pre-set threshold values.

[0019] In addition, the step of converting the growth status score involves applying weights to the individual scores converted for each of the plurality of variables and then summing them to convert the growth status score, and the weights may be set differently for each variable according to the importance of the variable.

[0020] In addition, the above method for automatically adjusting the nutrient solution concentration in hydroponic cultivation may further include a step of providing growth status score trend data using the growth status scores of the crop analyzed by date.

[0021] In addition, the above-mentioned adjusting step can adjust the nutrient solution concentration and cultivation environment conditions by using a control action corresponding to the classified grade among a plurality of control actions.

[0022] In addition, the above method for automatically adjusting the nutrient concentration in hydroponic cultivation may further include a step of predicting the shipment time of the crop by applying the type of crop, current life cycle, scored growth status score, and classified current growth status to an AI-based pre-trained prediction model.

[0023] In addition, the above-mentioned adjusting step may further include a step of increasing, maintaining, or decreasing the growth rate of the crop by controlling the nutrient solution concentration and cultivation environment conditions based on the result of comparing the predicted crop shipment time with a preset target shipment time. Effects of the invention

[0024] According to the present invention, the growth state of a crop can be estimated from a currently captured crop image based on sample images of the crop's life cycle and image processing, thereby adaptively adjusting the nutrient solution concentration and cultivation environment conditions according to the crop's current growth state.

[0025] In addition, in the case of the present invention, by prescribing customized nutrient solution concentration and cultivation environment conditions according to the current growth state of the crop, crops can be harvested quickly with high marketability, and the consumption of external resources required for cultivation can be reduced, thereby lowering production costs and ensuring the economic viability of farm operations. Brief explanation of the drawing

[0026] FIG. 1 is a diagram showing the configuration of an automatic hydroponic cultivation environment control system according to the life cycle developmental state of a plant factory type crop according to an embodiment of the present invention. FIG. 2 is a diagram that explains in detail the configuration of an automatic hydroponic environment control system according to an embodiment of the present invention. Figure 3 is a diagram that explains in detail the configuration of the developmental state analysis unit shown in Figure 2. Figure 4 is a diagram illustrating score trend data according to the growth status by life cycle for each crop according to an embodiment of the present invention. FIG. 5 is a diagram exemplarily illustrating the operation concept of an automatic hydroponic environment control system according to an embodiment of the present invention. FIG. 6 is a diagram illustrating a method for automatically controlling a hydroponic cultivation environment according to an embodiment of the present invention. FIG. 7 is a diagram illustrating a growth rate control method using crop shipment time prediction in an embodiment of the present invention. Specific details for implementing the invention

[0027] Then, with reference to the attached drawings, embodiments of the present invention will be described in detail so that those skilled in the art can easily implement the invention. However, the present invention may be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present invention in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification have been given similar reference numerals.

[0028] Throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are "directly connected," but also cases where they are "electrically connected" with other components interposed between them. Furthermore, when a part is described as "including" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0029] FIG. 1 is a diagram showing the configuration of an automatic hydroponic cultivation environment control system according to the life cycle developmental state of a plant factory type crop according to an embodiment of the present invention.

[0030] As shown in FIG. 1, the hydroponic environment automatic control system (100) according to an embodiment of the present invention can communicate with each other via a network connection with a camera (10) and an administrator terminal (200). The network may include a wired, wireless, or combined wired and wireless network.

[0031] A camera (10) may be installed in at least one plant factory-type cultivation facility to acquire images of crops being cultivated and transmit them to a system (100). The camera (10) may acquire images of target crops at set intervals and periodically transmit them to the system (100).

[0032] The camera (10) can be installed and operated in each management unit zone of the cultivation facility, and can be installed individually for each type of crop. Various embodiments of the operation and placement of the camera may exist.

[0033] The administrator terminal (200) may correspond to a terminal for facility managers or related parties, and may correspond to a desktop, PC, tablet PC, laptop, smartphone, smart pad, server of a related organization, etc.

[0034] The hydroponic environment automatic control system (100) can analyze the growth status of crops by imaging processing crop images collected through a camera (10) and provide the results of the analysis to an administrator terminal (200) as a push alarm, and can provide statistical analysis data for individual crops by date or by life cycle.

[0035] The hydroponic cultivation environment automatic control system (100) according to an embodiment of the present invention can classify the growth status grade of a crop and control the operation of various equipment or devices required for crop cultivation differently based on control actions determined according to the classified grade.

[0036] Here, the hydroponic environment automatic control system (100) classifies the current state of the crop into one of the grades of low growth, normal growth, and abnormal growth, and can provide a prescription with different nutrient solution concentrations and cultivation environment conditions according to the classified grade.

[0037] In the embodiments of the present invention, low growth may refer to a case where there are no defects in the condition of the crop but growth is slow compared to a normal sample of the corresponding life cycle, and abnormal growth may refer to a case where an abnormal condition or characteristic caused by defects, diseases, etc. is found.

[0038] The hydroponic cultivation environment automatic control system (100) according to the embodiment of the present invention can control the operation of various facilities and devices required for crop cultivation, and can control and standardize the growth speed of crops by presenting a customized prescription according to the growth state of the crops, as well as restore crops in an abnormal state, and as a result, can standardize the quality and timing of shipment of crops to increase production efficiency.

[0039] FIG. 2 is a diagram that explains in detail the configuration of an automatic hydroponic environment control system according to an embodiment of the invention.

[0040] As shown in FIG. 2, the hydroponic environment automatic control system (100) includes a data input unit (110), a growth state analysis unit (120), and a control unit (130), and may further include a statistics unit (140), a shipment time prediction unit (150), and a data storage unit (160). Here, the operation of each unit (110, 120, 140, 150, 160) and the data flow between each unit can be controlled by the control unit (130).

[0041] The data input unit (110) receives crop data including a crop image capturing the current growth state of the crop, the type of crop, and the current life cycle of the crop. The data input unit (110) may receive crop data through input means such as a mouse, keyboard, or touch screen, and when a crop image is received from the camera (10), the type of the crop and the current life cycle may be automatically entered into the data input unit (110) along with the crop image according to pre-set information.

[0042] Of course, the data input unit (110) can receive crop data from a network-connected administrator terminal (200) through operations such as a mouse, keyboard, or touch screen. At this time, the type of crop may correspond to information pre-entered or set by the user, and the current life cycle may correspond to information that is automatically counted by a timer based on the initial setting time for the crop. In this case, when a crop image is received, the type of crop and the current life cycle can be automatically set and entered.

[0043] The data input unit (110) can transmit crop data to the growth status analysis unit (120).

[0044] The growth status analysis unit (120) can classify the growth status of a crop into one of a plurality of grades by scoring the current growth status of the crop from the input crop data based on the pre-established life cycle sample image data for each crop.

[0045] Here, sample image data for each crop, established by life cycle, can be pre-stored in the data storage unit (160). Here, sample image data may correspond to previously observed sample images for each life cycle of a crop that has grown normally. That is, sample image data of a crop by life cycle may refer to images observed by each life cycle of a commercially viable crop that has grown normally. Here, the data storage unit (160) can store, modify, and update sample images for each crop by life cycle for each type of crop. Here, the life cycle may be distinguished by date or period.

[0046] The growth status analysis unit (120) can extract a sample image that matches the life cycle of the crop from among the sample images of the crop stored in the data storage unit (160) based on the currently input crop data. Then, the growth status analysis unit (120) can score the growth status of the current crop based on the result of comparing the characteristics of each variable extracted from the currently input crop image with the characteristics of each variable extracted from the sample image.

[0047] The growth status analysis unit (120) can classify the current growth status of the crop into one of the grades of low growth, normal growth, and abnormal growth by comparing the growth status score with a pre-set multi-stage threshold level.

[0048] Here, low growth may refer to cases where growth is slow compared to a normal sample for the corresponding life cycle, even though it is not abnormal growth, and abnormal growth may refer to cases where growth is abnormal due to nutritional deficiencies, pests, diseases, etc.

[0049] Figure 3 is a diagram that explains in detail the configuration of the developmental state analysis unit shown in Figure 2.

[0050] As shown in FIG. 3, the developmental state analysis unit (120) may include a feature extraction unit (121), a data extraction unit (122), a conversion unit (123), and a grade classification unit (124).

[0051] The feature extraction unit (121) can extract feature values ​​for multiple variables related to the growth state of the crop from the crop image. Here, the multiple variables may include the overall size, leaf color, stem thickness, presence or absence of fruit and presence or absence of flower.

[0052] The types and amounts of nutrients that crops can absorb vary slightly depending on their life cycle, and the environmental factors that need to be controlled may differ based on the condition of the crop's leaves, stems, and size. Considering these factors, crop size, leaf color, stem thickness, and the presence or absence of fruit can be extracted as characteristic variables.

[0053] Here, to extract feature values ​​for multiple variables, image processing including background removal, leaf extraction, stem extraction, and whole crop image extraction can be performed from the captured image.

[0054] The feature extraction unit (121) can analyze the color of the leaves of the crop. In this case, it can analyze whether the leaves are healthy and dark green, whether they are wilted or not even yellow, and whether there is no browning. In addition, the feature extraction unit (121) can analyze the thickness of the stem of the crop. At this time, it can analyze the degree to which the stem is thick and strong.

[0055] Additionally, the feature extraction unit (121) can analyze whether flowers or fruits have formed on the crop. Since some plants reproduce through flowers and fruits, it is necessary to determine whether there are flower or fruit products. Additionally, the feature extraction unit (121) can analyze the overall size (height, height) of the plant.

[0056] The data extraction unit (122) can search for and extract target sample image data that matches the type of crop currently entered and the current life cycle among the pre-established life cycle sample images for each crop stored in the data storage unit (160). The data extraction unit (122) can provide the target sample image data to the feature extraction unit (121) so that multiple variable-specific feature values ​​for the target sample image can be extracted.

[0057] The conversion unit (123) can convert the growth status score of a crop by comparing the feature values ​​for each variable extracted from the target sample image data with the feature values ​​for each variable extracted from the input crop image and combining the individual scores converted for each variable.

[0058] Specifically, the conversion unit (123) can convert the crop growth status score by applying weights to individual scores converted for multiple variables and then summing them all up. Here, the weights can be set differently for each variable depending on the importance of the variable.

[0059] Of course, the weights applied to multiple variables can be set differently depending on the type of crop being analyzed.

[0060] For example, depending on the type of crop, as an example of weights for each variable regarding leaf color, stem thickness, overall size, and presence / absence of flowers / fruits, for crop A, it may be set to "Leaf: 40%, Stem: 20%, Thickness: 10%, Size: 30%, Presence / absence of flowers (fruits): 0%", for crop B, it may be set to "Leaf: 30%, Stem: 10%, Thickness: 15%, Size: 15%, Presence / absence of flowers (fruits): 30%", and for crop C, it may be set to "Leaf: 40%, Stem: 10%, Thickness: 10%, Size: 40%, Presence / absence of flowers (fruits): 0%".

[0061] The grade classification unit (124) can classify the growth status of the crop into one of the grades of low growth, normal growth, and abnormal growth by comparing the quantified growth status score with a plurality of preset threshold values. The classification result derived from the growth status analysis unit (120) can be transmitted to the control unit (130).

[0062] For example, multiple thresholds may include a first threshold and a second threshold greater than the first. If the growth status score of a crop is less than the first threshold, it may be classified as an abnormal growth grade; if it is greater than or equal to the first threshold but less than the second threshold, it may be classified as a low growth grade; and if it is greater than or equal to the second threshold, it may be classified as a normal growth grade. For example, the growth status score may be converted to a range between 0 and 100 points, and each threshold may have a value between 0 and 100.

[0063] The control unit (130) can adjust the control conditions applied to the crop according to the classified growth state, namely the nutrient solution concentration and the cultivation environment conditions. Here, the control elements may include the nutrient solution concentration for each nutrient solution (e.g., nutrient solution A, nutrient solution B, nitric acid, etc.), the raw water temperature, the ambient temperature, the ambient humidity, the lighting (LED) intensity, the lighting duration, and the degree of raw water dilution. The remaining conditions, excluding the nutrient solution concentration, may refer to the cultivation environment conditions.

[0064] Here, the control conditions (nutrient solution concentration and cultivation environment conditions) determined according to the growth grade of each crop's life cycle for each crop type may correspond to data derived from the results of prior learning using historical big data. That is, in the embodiments of the present invention, optimal control conditions for each crop type, life cycle, and condition grade can be pre-established based on big data.

[0065] Here, the control unit (130) can control the nutrient solution concentration and cultivation environment conditions by using a control action corresponding to the currently classified grade among a plurality of control actions.

[0066] If the growth status of the crop is classified as low growth, a first control action (ACT A) corresponding to the low growth grade is executed; if it is classified as normal growth, a second control action (ACT B) corresponding thereto is executed; and if it is classified as abnormal growth, a third control action (ACT C) corresponding thereto is executed, thereby controlling the operation of various equipment and devices within the facility according to the chemical solution concentration and cultivation environment conditions set corresponding to each respective action.

[0067] For example, in the case of a low-growth grade, the chemical solution concentration and cultivation environment conditions can be adjusted according to the first control action to conditions for accelerating crop growth, thereby further promoting and accelerating crop growth; in the case of a normal growth grade, the current chemical solution concentration and cultivation environment conditions can be maintained; and in the case of an abnormal growth grade, the chemical solution concentration and cultivation environment conditions can be adjusted to conditions for crop recovery, thereby resolving nutrient deficiencies, nutrient excess, and pests and diseases, so that the crop can be recovered.

[0068] In this way, in the case of the embodiment of the present invention, a customized prescription can be provided by analyzing the growth status of the crop at each stage of its life cycle until harvesting using image processing technology.

[0069] The statistics department (140) can provide growth status score trend data using the growth status scores of crops analyzed by date. Of course, through this, the growth status by life cycle can be checked.

[0070] Figure 4 is a diagram illustrating score trend data according to the growth status by life cycle for each crop according to an embodiment of the present invention. In Figure 4, the X-axis represents the date, and the Y-axis represents the growth status score derived for each date. Figure 4 shows the trend of growth status scores analyzed by date for crops A, B, and C, and allows one to confirm the change in the growth status of the crops according to the life cycle on a date.

[0071] Table 1 below shows specific examples of actions taken in the cases of low growth, normal growth, and abnormal growth.

[0072] Classification results LOOP (Action) low growth [ACTION A] Operate the heating / cooling system to keep the water temperature within the 20-25℃ range. Dilute the pH of the nutrient solution using nitrate to keep it within the 5.5-6.5 range. Analyze the lighting intensity and adjust the light intensity or lighting time if it falls below the light compensation point. Normal growth [ACTION B] Operate via loopback of the existing control system Abnormal growth [ACTION C] A. Cases where rehabilitation is possible A-1. "XX Condition Detected, Restoration System Activated" based on crop status analysis of abnormal cases ('XX Condition': Type of abnormal condition / Activate Crop Restoration Loop depending on the condition) A-2. Analyze image status after 24 hours A-3. If the score falls within the normal growth range, activate the normal growth loop. A-3-1. If the score falls within the abnormal growth range, restart with Loop A. B. Cases where rehabilitation is impossible B-1. Shut down the automatic environmental control system. B-2. Dispose of crops or use them as fertilizer. B-3. Precise analysis of raw water and re-analysis of various environmental factors. B-4. Analyze whether the environment is within the optimal range for crop growth. B-4-1. If within the range, proceed to B-5. B-4-2. If not within the range, proceed to B-3. B-5. Transplant new crops. B-6. Activate the existing control system via loopback.

[0073] Here, abnormal growth may occur due to tip burn, midrib scorching, brown spot disease, abnormal head formation, sunscald damage, cracking, nutrient deficiency, nutrient excess, etc. In cases of abnormal growth where recovery is impossible, the crop may be disposed of and the raw water analyzed for the analysis of primary nutrients.

[0074] Here, the action loop applied to each case is not necessarily limited to Table 1, and various other embodiments may exist.

[0075] The shipment time prediction unit (150) can predict the shipment time of the crop by applying the type of crop, the scored growth status score, and the classified current growth status to an AI-based pre-trained prediction model. The predicted shipment time can be provided to the control unit (130).

[0076] The prediction model can be trained in advance based on deep learning algorithms using historical big data on crops that have been shipped, with crop type, current life cycle, growth status score, and growth status grade as input data and actual shipment time as output data. This prediction model can be implemented using algorithms such as multiple regression models and deep learning neural networks.

[0077] Additionally, the control unit (130) may control the nutrient solution concentration and cultivation environment conditions based on the result of comparing the predicted crop shipment time with a preset target shipment time, thereby increasing, maintaining, or decreasing the growth rate of the crop. For example, if the shipment time is earlier than the target shipment time, the nutrient solution concentration and cultivation environment conditions may be set in a direction that reduces the growth rate, and if the shipment time is later than the target shipment time, the nutrient solution concentration and cultivation environment conditions may be set in a direction that increases the growth rate in response.

[0078] FIG. 5 is a diagram exemplarily showing the operation concept of an automatic hydroponic environment control system according to an embodiment of the present invention.

[0079] The hydroponic cultivation environment automatic control system (100) receives crop image data generated by a camera and analyzes the growth status of the crop by comparing the input crop image data with previously stored sample crop image data. At this time, if the growth status is determined to be low growth, ACT A is executed; if it is determined to be normal growth, ACT B is executed; and if it is determined to be abnormal growth, ACT C is executed, thereby controlling the operation of various equipment and devices within the facility with control conditions (chemical solution concentration and cultivation environment conditions) corresponding to the corresponding action. This operation can be repeated at intervals of set times (e.g., 12 o'clock, 24 o'clock).

[0080] In addition, the hydroponic cultivation environment automatic control system (100) can store crop images taken on the previous day in a cloud server (300), analyze the crop growth potential compared to the previous day by analyzing the currently input crop image and the crop images from the previous day stored in the cloud server (300), and can provide the growth potential analysis data analyzed daily as statistical data through an administrator terminal. Of course, the cloud server (300) can store image data taken on each crop by date and use it to build and update sample data for each crop's life cycle.

[0081] FIG. 6 is a diagram illustrating a method for automatically controlling a hydroponic cultivation environment according to an embodiment of the present invention.

[0082] First, the hydroponic environment automatic control system (100) receives crop data including a crop image of the current growth state of the crop, the type of crop, and the current life cycle of the crop (S610).

[0083] Next, the hydroponic environment automatic control system (100) scores the growth status of the crop based on the currently input crop data, using pre-established life cycle sample image data for each crop (S620), and classifies the current growth status of the crop into one of a plurality of grades based on the converted growth status score (S630).

[0084] Then, the hydroponic cultivation environment automatic control system (100) controls the nutrient solution concentration prescribed to the crop and cultivation environment conditions according to the classified growth status grade (S640). Here, the hydroponic cultivation environment automatic control system (100) can provide the growth status grade classified for each crop, captured images, control action information, etc. to the administrator terminal (200).

[0085] Next, the hydroponic environment automatic control system (100) provides score trend statistical data to the administrator terminal (200) using the growth status score data accumulated by day (S650).

[0086] In the case of an embodiment of the present invention, the future shipping time of the crop may be predicted based on the growth status grade derived from the current life cycle of the crop through step S630, and the growth rate of the crop may be controlled by changing the control conditions based on the result of comparing the predicted shipping time with the target shipping time.

[0087] FIG. 7 is a diagram illustrating a growth rate control method using crop shipment time prediction in an embodiment of the present invention. The process of FIG. 7 may be performed at any point after step S630.

[0088] As shown in FIG. 7, the hydroponic environment automatic control system (100) collects input variable data including the type of crop, a scored growth status score, a current life cycle, and a classified current growth status grade (S710).

[0089] And, the hydroponic environment automatic control system (100) predicts the timing of crop shipment by applying the collected input variable data to a pre-trained prediction model based on artificial intelligence (S720).

[0090] Then, the hydroponic environment automatic control system (100) compares the predicted shipping time with the target shipping time and adjusts the control conditions (nutrient solution concentration, cultivation environment conditions) (S730). In this way, the growth rate of the crop can be controlled to match the target shipping time.

[0091] According to the present invention as described above, the growth state of a crop can be estimated from a currently captured crop image based on sample images of the crop's life cycle and image processing, and the nutrient solution concentration and cultivation environment conditions can be adaptively adjusted and changed according to the current growth state.

[0092] In addition, in the case of the present invention, by prescribing customized nutrient solution concentration and cultivation environment conditions according to the current growth state of the crop, crops can be harvested quickly with high marketability, and production costs can be lowered and the economic efficiency of farm operations can be secured by reducing the consumption of external resources required for cultivation, such as LED lighting, heating and cooling systems, and nutrient solutions.

[0093] The present invention has been described with reference to embodiments illustrated in the drawings, but this is merely illustrative, and those skilled in the art will understand that various modifications and equivalent alternative embodiments are possible therefrom. Accordingly, the true technical scope of protection of the present invention should be determined by the technical spirit of the appended claims. Explanation of the symbols

[0094] 100: Automatic hydroponic environment control system 110: Data Input Unit 120: Development Status Analysis Unit 121: Feature extraction unit 122: Data extraction unit 123: Conversion Section 124: Grade Classification Section 130: Control Unit 140: Statistics Unit 150: Shipment time prediction unit 160: Data storage unit

Claims

Claim 1 In an automatic hydroponic cultivation environment control system based on the life cycle developmental state of a plant factory type crop, the system comprises: a data input unit that receives crop data including a crop image capturing the current developmental state of the crop, the type of crop, and the current life cycle of the crop; a developmental state analysis unit that scores the current developmental state of the crop based on the input crop data and classifies it into one of a plurality of grades based on pre-established sample image data for each crop by life cycle; and a shipment time prediction unit that predicts the shipment time of the crop by applying the type of crop, the current life cycle, the scored developmental state score, and the classified current developmental state to a pre-trained prediction model based on artificial intelligence. The hydroponic environment automatic control system includes a control unit that controls the nutrient solution concentration and cultivation environment conditions applied to the crop according to the classified growth state, wherein the growth state analysis unit includes: a feature extraction unit that extracts feature values ​​for a plurality of variables related to the growth state of the crop from the crop image; a data extraction unit that extracts target sample image data matched with the type of crop and the current life cycle from pre-established life cycle sample images for each crop; a conversion unit that calculates a growth state score of the crop by combining individual scores converted for each variable by comparing the feature values ​​for each variable extracted from the target sample image data with the feature values ​​for each variable extracted from the input crop image; and a grade classification unit that classifies the growth state of the crop into one of low growth, normal growth, and abnormal growth by comparing the quantified growth state score with a plurality of pre-set threshold values. The control unit controls the nutrient solution concentration and cultivation environment conditions based on the result of comparing the predicted crop shipment time with a pre-set target shipment time to increase, maintain, or decrease the growth rate of the crop. Claim 2 delete Claim 3 A hydroponic cultivation environment automatic control system according to claim 1, wherein a plurality of variables related to the growth state of the crop include at least one of the total size of the crop, leaf color, stem thickness, presence or absence of fruit, and presence or absence of flower. Claim 4 In claim 1, the conversion unit converts the growth status score by applying weights to individual scores converted for each of the plurality of variables and summing them, wherein the weights are set differently for each variable according to the importance of the variable, in an automatic hydroponic environment control system. Claim 5 In claim 4, an automatic hydroponic environment control system in which the weights applied to each of the plurality of variables are set differently according to the type of crop. Claim 6 The hydroponic environment automatic control system according to claim 1, further comprising a statistics unit that provides growth status score trend data using the growth status score of the crop analyzed by date. Claim 7 In claim 1, the control unit controls the nutrient solution concentration and cultivation environment conditions using a control action corresponding to the classified grade among a plurality of control actions, thereby forming an automatic hydroponic environment control system. Claim 8 delete Claim 9 delete Claim 10 A method for automatically controlling hydroponic nutrient solution concentration performed by an automatic hydroponic environment control system, comprising: a step of receiving crop data including a crop image capturing the current growth state of a plant factory-type crop, the type of crop, and the current life cycle of the crop; a step of scoring the current growth state of the crop from the input crop data based on pre-established sample image data by life cycle for each crop and classifying it into one of a plurality of grades; and a step of predicting the shipment time of the crop by applying the type of crop, the current life cycle, the scored growth state score, and the classified current growth state to a pre-trained prediction model based on artificial intelligence. A method for automatically controlling nutrient solution concentration in hydroponics, comprising the step of controlling the nutrient solution concentration and cultivation environment conditions applied to the crop according to the classified growth state, wherein the step of classifying the growth state of the crop includes: a step of extracting feature values ​​for a plurality of variables related to the growth state of the crop from the crop image; a step of extracting target sample image data matched with the type of crop and the current life cycle from among pre-established life cycle sample images for each crop; a step of calculating a growth state score of the crop by combining individual scores converted for each variable by comparing the feature values ​​for each variable extracted from the target sample image data with the feature values ​​for each variable extracted from the input crop image; and a step of classifying the growth state of the crop into one of low growth, normal growth, and abnormal growth by comparing the quantified growth state score with a plurality of pre-set threshold values, and wherein the controlling step further includes the step of controlling the nutrient solution concentration and cultivation environment conditions based on the result of comparing the predicted crop shipment time with a pre-set target shipment time to increase, maintain, or decrease the growth rate of the crop. Claim 11 delete Claim 12 In claim 10, the step of converting the growth status score is to convert the growth status score by applying weights to individual scores converted for each of the plurality of variables and then summing them up, wherein the weights are set differently for each variable according to the importance of the variable, a method for automatically controlling the concentration of hydroponic nutrient solution. Claim 13 A method for automatically controlling hydroponic nutrient solution concentration according to claim 10, further comprising the step of providing growth status score trend data using the growth status score of the crop analyzed by date. Claim 14 In claim 10, the adjusting step is a method for automatically adjusting the nutrient solution concentration and cultivation environment conditions using a control action corresponding to the classified grade among a plurality of control actions. Claim 15 delete Claim 16 delete

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