Strawberry stress index calculation method and cultivation management system using chlorophyll fluorescence value

Abstract

The present invention relates to a strawberry stress index calculation method and cultivation management system using a chlorophyll fluorescence value, the method including the steps of: collecting, by means of a data collection unit and in combination with a chlorophyll fluorescence analysis apparatus, chlorophyll fluorescence analysis data including fluorescence analysis images of strawberries; collecting, by means of the data collection unit, growth data regarding the growth stage of strawberries, divided into shoot system and root system, the growth data including the growth images of strawberries and matching the chlorophyll fluorescence analysis data; collecting, by means of the data collection unit, cultivating environment data regarding cultivating environment conditions of shoot system and root system in the growth stage of strawberries; establishing, by means of a model establishment unit, an artificial intelligence (AI) model predicting a stress index of strawberries according to the growth stage and cultivating environment conditions of shoot system and root system by learning the growth data and the cultivating environment conditions data, divided into shoot system and root system, together with the chlorophyll fluorescence analysis data regarding strawberries; and predicting, by means of a stress index prediction unit and utilizing the AI model, the stress index of the strawberries currently being cultivated according to the growth stage and cultivating environment conditions.

Description

A method for calculating the stress index of strawberries using chlorophyll fluorescence values ​​and a cultivation management system.

[0001] The present invention relates to a stress management technology, and more particularly, to a method for calculating a stress index of strawberries using chlorophyll fluorescence values ​​and a cultivation management system, which take into account the characteristics of strawberries that are sensitive to stress and derive objective quantitative indicators of strawberry stress based on chlorophyll fluorescence analysis of plants, growth data and images, and cultivation environment data.

[0002]

[0003]

[0004] Strawberries are perennial plants belonging to the rose family, and it is known that the strawberries currently cultivated around the world are the result of accidental hybridization between the South American wild species Fragaria chiloensis and the North American wild species Fragaria virginiana. Although strawberries require a long cultivation period and a lot of labor, they grow well even in low temperatures, so heating costs are almost non-existent during the winter cultivation period, and management costs are relatively low excluding the labor required for harvesting and sorting.

[0005] Strawberry cultivation, in particular, is gradually decreasing due to the demanding labor involved in seedling cultivation, harvesting, and sorting, as well as the aging and declining agricultural workforce. However, per-acre yields are steadily increasing, driven by the widespread adoption of high-yield varieties and the expansion of hydroponic cultivation. The cultivation area is expected to decline somewhat in the future, and productivity may be improved through the continued development of high-yield varieties, labor-saving measures, and facility modernization.

[0006] Meanwhile, when growing strawberries hydroponically, not only can the operating costs be reduced by increasing and reducing the use of fertilizers through efficient nutrient solution cultivation, but also the burden on the environment can be reduced. In addition, pests and diseases introduced through the soil and problems arising from the soil can be prevented at the source, preventing the use of pesticides, enabling the production of healthier and more environmentally friendly strawberries without pesticides. In addition, hydroponics, which has a synergistic effect with environmental control technology, can be actively utilized in vertical farms, indoor farming, and urban agriculture.

[0007] Above all, since strawberries are a crop that is very sensitive to stress, stress management of the plant is very important for hydroponic cultivation of strawberries, and techniques for this may be essential.

[0008] [Prior Art Literature]

[0009] [Patent Document]

[0010] Korean Patent Publication No. 10-2016-0052367 (May 12, 2016)

[0011]

[0012] One embodiment of the present invention is to measure the direct response and stress level of a plant according to a cultivation environment through chlorophyll fluorescence analysis, and more specifically, to objectively measure the stress of a plant, a chlorophyll fluorescence analyzer is used to precisely measure the F value, and a chlorophyll fluorescence AI model is used to analyze which part of various environmental variables causes the stress, thereby enabling real-time stress control in plant cultivation, thereby providing a method for calculating a stress index of strawberries using chlorophyll fluorescence values ​​and a cultivation management system.

[0013]

[0014] Among the embodiments, a method for calculating a stress index of a strawberry using a chlorophyll fluorescence value includes the steps of: collecting chlorophyll fluorescence analysis data including a fluorescence analysis image of a strawberry through a data collection unit in conjunction with a chlorophyll fluorescence analysis device; collecting growth data regarding the growth stages of each above-ground part and each below-ground part of the strawberry, which includes a growth image of the strawberry and matches the chlorophyll fluorescence analysis data, through the data collection unit; collecting cultivation environment data regarding cultivation environment conditions of each above-ground part and each below-ground part during the growth process of the strawberry through the data collection unit; building an artificial intelligence (AI) model for predicting a stress index of the strawberry according to the growth stages and cultivation environment conditions of each above-ground part and each below-ground part by learning the growth data and the cultivation environment data divided into the above-ground part and the below-ground part together with the chlorophyll fluorescence analysis data associated with the strawberry through a model construction unit; and predicting a stress index according to the growth stage and cultivation environment conditions of a strawberry currently being cultivated using the AI ​​model through a stress index prediction unit.

[0015] The step of collecting the above chlorophyll fluorescence analysis data may include the step of extracting a plurality of chlorophyll fluorescence parameters from the above fluorescence analysis image of the true leaf of the strawberry.

[0016] The step of collecting the growth data separately may include a step of collecting above-ground growth data including the fresh weight, leaf area, leaf length, leaf width, and number of leaves of the strawberry based on the above-ground part, and below-ground growth data including the fresh weight, length and diameter of the main root, and ratio of fine roots of the strawberry based on the below-ground part.

[0017] The step of collecting the above-mentioned cultivation environment data may include a step of collecting first cultivation environment data including temperature, humidity, VPD, light quantity, and light spectrum as cultivation environment conditions of the above-ground part, and second cultivation environment data including the ratio and concentration of nutrient solution, EC, pH, temperature of the nutrient solution, and irrigation cycle as cultivation environment conditions of the underground part.

[0018] The step of constructing the artificial intelligence model includes a step of constructing the artificial intelligence model by learning at least a portion of a dataset including the chlorophyll fluorescence analysis data, a first combination between the above-ground growth data and the underground growth data, and a second combination between the first cultivation environment data and the second cultivation environment data, and the artificial intelligence model can be learned to predict and output a stress factor among the growth stage and cultivation environment conditions of the strawberry together with the stress index according to the data configuration of the dataset used in the learning process.

[0019] Among the embodiments, a strawberry cultivation management system using a stress index includes: a data collection unit that collects chlorophyll fluorescence analysis data including a fluorescence analysis image of a strawberry in conjunction with a chlorophyll fluorescence analysis device; growth data regarding the growth stages of each of the above-ground and underground parts of the strawberry that includes a growth image of the strawberry and matches the chlorophyll fluorescence analysis data; and cultivation environment data regarding the cultivation environment conditions of each of the above-ground and underground parts during the growth process of the strawberry; a model construction unit that learns the growth data and the cultivation environment data divided into the above-ground and underground parts together with the chlorophyll fluorescence analysis data associated with the strawberry to construct an artificial intelligence (AI) model that predicts the stress index of the strawberry according to the growth stages and cultivation environment conditions of each of the above-ground and underground parts; a stress index prediction unit that predicts the stress index according to the growth stage of the strawberry currently being cultivated using the AI ​​model; and a cultivation environment control unit that actively controls the cultivation environment of the strawberry currently being cultivated based on the stress index.

[0020] The above cultivation environment control unit can control the cultivation environment in a direction to reduce the overall stress of the strawberry during the root establishment and growth period of the strawberry based on the stress index, or can control the cultivation environment in a direction to enhance a specific stress of the strawberry during the flower differentiation period of the strawberry.

[0021]

[0022] The disclosed technology may have the following effects. However, this does not mean that a particular embodiment must include all or only the following effects, and therefore the scope of the disclosed technology should not be construed as being limited thereby.

[0023] A method for calculating a stress index of strawberries using chlorophyll fluorescence values ​​and a cultivation management system according to one embodiment of the present invention matches not only environmental data such as temperature, humidity, light quantity, and light quality, but also the phenotype of a plant with fluorescence analysis data through image data, and generates a chlorophyll fluorescence AI model by performing artificial intelligence learning on the matched data, and analyzes the degree of stress in the plant and the cause thereof by utilizing the chlorophyll fluorescence AI model, analyzes the stress in the plant in real time by utilizing the chlorophyll fluorescence AI model, and performs preemptive stress control through AI prediction, thereby enabling effective plant cultivation.

[0024] In addition, the present invention can not only increase production and quality, but also find the optimal conditions for plants that are currently difficult to cultivate due to real-time stress management, and enable stress management, thereby enabling the cultivation of high-value-added plants. In the future, the present invention can be utilized to effectively optimize the creation of stress management AI models for high-value-added plants other than strawberries with less data by applying transfer learning technology, thereby reducing the time and effort required for stress cultivation experiments for new high-value-added plants. In addition, it is possible to reverse-predict the level of stress in plants through environmental conditions and image data, and to simulate how they will grow, which can be applied to digital twin technology. By utilizing digital twins, research that would have required decades of repetitive cultivation processes can be performed and predicted in a short period of time through simulations of digital twins without conducting direct cultivation experiments, effectively reducing the research period.

[0025]

[0026] Figure 1 is a drawing explaining a cultivation management system according to the present invention.

[0027] Fig. 2 is a drawing explaining the system configuration of the stress index calculation device of Fig. 1.

[0028] Fig. 3 is a drawing explaining the functional configuration of the stress index calculation device of Fig. 1.

[0029] Figure 4 is a flowchart explaining a method for calculating a stress index of strawberries using chlorophyll fluorescence values ​​according to the present invention.

[0030] FIG. 5 is a drawing illustrating one embodiment of a fluorescence analysis image of a strawberry according to the present invention.

[0031] Figure 6 is a drawing illustrating one embodiment of an artificial intelligence model according to the present invention.

[0032]

[0033] The description of the present invention is merely an example for structural and functional explanation, and therefore, the scope of the present invention should not be construed as being limited by the embodiments described in the text. That is, since the embodiments can be modified in various ways and can take various forms, the scope of the present invention should be understood to include equivalents that can realize the technical idea. In addition, the purposes or effects presented in the present invention do not mean that a specific embodiment must include all of them or only such effects, and therefore, the scope of the present invention should not be construed as being limited thereby.

[0034] Meanwhile, the meaning of the terms described in this application should be understood as follows.

[0035] Terms such as "first" and "second" are intended to distinguish one component from another, and the scope of the rights should not be limited by these terms. For example, the first component may be referred to as the second component, and similarly, the second component may also be referred to as the first component.

[0036] When a component is said to be "connected" to another component, it should be understood that while it may be directly connected to that other component, there may also be other components intervening. Conversely, when a component is said to be "directly connected" to another component, it should be understood that there are no other intervening components. Similarly, other expressions describing relationships between components, such as "between" and "directly between," or "adjacent to" and "directly adjacent to," should be interpreted similarly.

[0037] Singular expressions should be understood to include plural expressions unless the context clearly indicates otherwise, and terms such as "comprises" or "have" should be understood to specify the presence of a feature, number, step, operation, component, part or combination thereof, but not to exclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

[0038] For each step, the identifiers (e.g., a, b, c, etc.) are used for convenience of explanation and do not describe the order of the steps. The steps may occur in a different order than stated unless the context clearly dictates a specific order. That is, the steps may occur in the same order as stated, may be performed substantially simultaneously, or may be performed in the opposite order.

[0039] The present invention can be implemented as computer-readable code on a computer-readable recording medium. The computer-readable recording medium includes all types of recording devices that store data that can be read by a computer system. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices. Furthermore, the computer-readable recording medium can be distributed across network-connected computer systems, so that the computer-readable code can be stored and executed in a distributed manner.

[0040] Unless otherwise defined, all terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this invention pertains. Terms defined in commonly used dictionaries should be interpreted to be consistent with their meaning within the context of the relevant technology, and should not be interpreted as having an idealized or overly formal meaning unless explicitly defined herein.

[0041]

[0042] Figure 1 is a drawing explaining a cultivation management system according to the present invention.

[0043] Referring to FIG. 1, the cultivation management system (100) may include a chlorophyll fluorescence analysis device (110), a stress index calculation device (130), and a database (150).

[0044] The chlorophyll fluorescence analysis device (110) may correspond to a terminal device operated by a user. In the embodiment of the present invention, a user may be understood as one or more users, and each of the one or more users may correspond to one or more chlorophyll fluorescence analysis devices (110). That is, although FIG. 1 shows one chlorophyll fluorescence analysis device (110), it may correspond to a plurality of chlorophyll fluorescence analysis devices independently operated by a plurality of users, as needed.

[0045] In addition, the chlorophyll fluorescence analysis device (110) can be implemented as one device constituting the cultivation management system (100) according to the present invention, and the cultivation management system (100) can be implemented in various modified forms depending on the purpose of calculating the stress index of strawberries using chlorophyll fluorescence values.

[0046] In addition, the chlorophyll fluorescence analysis device (110) may be implemented to be operable by being connected to a stress index calculation device (130). Specifically, the chlorophyll fluorescence analysis device (110) may include a light source of a spectrum for measuring chlorophyll fluorescence, a filter for filtering only the chlorophyll fluorescence of a plant from reflected light, a camera for photographing the filtered chlorophyll fluorescence, and an image processing device for processing the acquired image information.

[0047] Here, the light source can be implemented as a light source with a specific spectrum. For example, a blue light source can be used to measure chlorophyll fluorescence in strawberries. The blue light source can be implemented as an LED light source, but this is not necessarily limited to this. Additionally, the filter can be implemented as a bandpass filter or an IR filter.

[0048] In addition, the chlorophyll fluorescence analysis device (110) can output various parameter values ​​as a result of chlorophyll fluorescence analysis. That is, the stress index calculation device (130) can receive chlorophyll fluorescence analysis data from the chlorophyll fluorescence analysis device (110) and store it in a database (150).

[0049] Meanwhile, the chlorophyll fluorescence analysis device (110) can be connected to the stress index calculation device (130) through a network, and a plurality of chlorophyll fluorescence analysis devices (110) can be connected to the stress index calculation device (130) simultaneously.

[0050] The stress index calculation device (130) may be implemented as a server corresponding to a computer or program that performs the method for calculating the stress index of strawberries using chlorophyll fluorescence values ​​according to the present invention. In addition, the stress index calculation device (130) may be connected to the chlorophyll fluorescence analysis device (110) via a wired network or a wireless network such as Bluetooth, WiFi, LTE, etc., and may transmit and receive data with the chlorophyll fluorescence analysis device (110) via the network. In addition, the stress index calculation device (130) may be implemented to operate while being connected to an independent external system (not shown in FIG. 1).

[0051] The database (150) may correspond to a storage device that stores various information required during the operation of the stress index calculation device (130). For example, the database (150) may store learning data for model construction and information for data preprocessing operations, or information regarding a learning algorithm for model construction. However, the database is not necessarily limited thereto, and the stress index calculation device (130) may store information collected or processed in various forms during the process of performing the method for calculating the stress index of strawberries using chlorophyll fluorescence values ​​according to the present invention.

[0052] In addition, in FIG. 1, the database (150) is depicted as a device independent of the stress index calculation device (130), but it is not necessarily limited thereto, and it can be implemented as a logical storage device included in the stress index calculation device (130).

[0053]

[0054] Fig. 2 is a drawing explaining the system configuration of the stress index calculation device of Fig. 1.

[0055] Referring to FIG. 2, the stress index calculation device (130) may include a processor (210), a memory (230), a user input / output unit (250), and a network input / output unit (270).

[0056] The processor (210) can execute a procedure for calculating a stress index of strawberries using chlorophyll fluorescence values ​​according to an embodiment of the present invention, manage a memory (230) that is read or written in this process, and schedule a synchronization time between volatile memory and non-volatile memory in the memory (230). The processor (210) can control the overall operation of the stress index calculating device (130), and is electrically connected to the memory (230), the user input / output unit (250), and the network input / output unit (270) to control data flow therebetween. The processor (210) can be implemented as a CPU (Central Processing Unit) or GPU (Graphics Processing Unit) of the stress index calculating device (130).

[0057] The memory (230) may include an auxiliary memory device implemented as a non-volatile memory such as an SSD (Solid State Disk) or an HDD (Hard Disk Drive) and used to store all data required for the stress index calculation device (130), and may include a main memory device implemented as a volatile memory such as a RAM (Random Access Memory). In addition, the memory (230) may store a set of commands that are executed by an electrically connected processor (210) to execute a method for calculating a stress index of strawberries using chlorophyll fluorescence values ​​according to the present invention.

[0058] The user input / output unit (250) includes an environment for receiving user input and an environment for outputting specific information to the user, and may include, for example, an input device including an adapter such as a touchpad, a touch screen, a visual keyboard, or a pointing device, and an output device including an adapter such as a monitor or a touch screen. In one embodiment, the user input / output unit (250) may correspond to a computing device connected via remote access, and in such a case, the stress index calculation device (130) may be performed as an independent server.

[0059] The network input / output unit (270) provides a communication environment for connecting to a chlorophyll fluorescence analysis device (110) via a network, and may include an adapter for communication such as a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), and a value added network (VAN). In addition, the network input / output unit (270) may be implemented to provide a short-distance communication function such as WiFi or Bluetooth, or a wireless communication function of 4G or higher for wireless transmission of data.

[0060]

[0061] Fig. 3 is a drawing explaining the functional configuration of the stress index calculation device of Fig. 1.

[0062] Referring to FIG. 3, the stress index calculation device (130) can perform a method for calculating a stress index of strawberries using chlorophyll fluorescence values ​​according to the present invention. To this end, the stress index calculation device (130) can include a data collection unit (310), a model construction unit (330), a stress index prediction unit (350), a cultivation environment control unit (370), and a control unit (not shown in FIG. 3).

[0063] At this time, the embodiments of the present invention do not need to include all of the above-described components simultaneously. Depending on the embodiment, some of the above-described components may be omitted, or some or all of the above-described components may be selectively included. The operation of each component will be described in detail below.

[0064] The data collection unit (310) can be linked to a chlorophyll fluorescence analysis device (110) to collect chlorophyll fluorescence analysis data including a fluorescence analysis image of a strawberry, growth data regarding the growth stages of each above-ground and underground part of the strawberry including a growth image of the strawberry and matching the chlorophyll fluorescence analysis data, and cultivation environment data regarding the cultivation environment conditions of each above-ground and underground part during the growth process of the strawberry.

[0065] In one embodiment, the data collection unit (310) can extract a plurality of chlorophyll fluorescence parameters from a fluorescence analysis image of a true leaf of a strawberry. Here, the true leaf may correspond to a leaf that emerges next after a young leaf emerges after germination among the leaves of the strawberry. The data collection unit (310) can receive the results of chlorophyll fluorescence analysis performed by the chlorophyll fluorescence analysis device (110). For example, the plurality of chlorophyll fluorescence parameters may correspond to values ​​expressed as quantified values ​​of chlorophyll fluorescence analysis, and may include Fm, Fv, Fo, Ft, Fq, Qmax, Fv / Fm, NPQ, qN, qP, qL, Rfd values, etc. In addition, the data collection unit (310) may also use a functional formula through a combination of a plurality of chlorophyll fluorescence parameters. Through this, the chlorophyll fluorescence value emitted from photosystem II (PS II) of strawberries can be expressed in terms of various chlorophyll fluorescence parameters.

[0066] In one embodiment, the data collection unit (310) may collect above-ground growth data including fresh weight, leaf area, leaf length, leaf width, and number of leaves of the strawberry based on the above-ground part of the strawberry, and may collect underground growth data including fresh weight, length and diameter of main root, and ratio of fine roots of the strawberry based on the underground part of the strawberry. The data collection unit (310) may collect growth data through growth survey of the strawberry, and may independently collect growth data by distinguishing between the above-ground part and the underground part of the strawberry.

[0067] That is, the above-ground growth data corresponds to growth data collected from the stems, leaves, and fruits of strawberries exposed above-ground, and the underground growth data may correspond to growth data collected from the stems and roots buried underground. Accordingly, the growth data used for predicting the stress index may be applied by dividing it into above-ground or underground parts, and the learning data used in the process of building an artificial intelligence model may also be applied by dividing it into above-ground growth data and underground growth data. In particular, the data collection unit (310) can focus on collecting growth data of strawberry roots, such as the growth of the main root or the ratio of fine roots (filamentous roots), and can be utilized for the actual establishment and growth of strawberries.

[0068] Meanwhile, the fresh weight of a strawberry corresponds to the weight of the plant, the leaf area corresponds to the area of ​​one side of a strawberry leaf, the leaf length corresponds to the length of a leaf, the side width corresponds to the width of a leaf, and the number of leaves may correspond to the number of leaves. The main root of a strawberry may correspond to the main root. The data collection unit (310) may be linked to a user terminal to periodically receive growth data of a strawberry measured by a user from the user terminal and then store the data in a database (150).

[0069] In one embodiment, the data collection unit (310) can match chlorophyll fluorescence analysis data and growth data for strawberries and then store them in a database (150). The data collection unit (310) can calculate the similarity between the fluorescence analysis images of the chlorophyll fluorescence analysis data and the growth images of the growth data, and can match the fluorescence analysis data and the growth data based on the similarity between the images. In addition, the data collection unit (310) can match the fluorescence analysis data and the growth data through various methods.

[0070] In one embodiment, the data collection unit (310) may collect first cultivation environment data including temperature, humidity, VPD (Vapor Pressure Deficit), light intensity, and light spectrum as the above-ground cultivation environment, and second cultivation environment data including nutrient solution ratio and concentration, EC (Electric Conductivity), pH (Hydrogen Ion Concentration), nutrient solution temperature, and irrigation cycle as the underground cultivation environment. To this end, the data collection unit (310) may collect cultivation environment data in conjunction with a user terminal and then store it in a database (150). The data collection unit (310) may collect cultivation environment data that can be collected above-ground and cultivation environment data that can be collected underground by distinguishing between them. Accordingly, cultivation environment data used for stress index prediction may be applied by distinguishing between above-ground and underground cultivation environment data, and learning data used in the process of constructing an artificial intelligence model may also be applied by distinguishing between above-ground cultivation environment data and underground cultivation environment data.

[0071] Additionally, the data collection unit (310) may operate in conjunction with various sensors installed above or below ground in the cultivation space to directly collect cultivation environment data regarding cultivation environment conditions. For example, the data collection unit (310) may collect cultivation environment data through temperature sensors, humidity sensors, EC sensors, pH sensors, dissolved oxygen sensors, ion sensors, light sensors, etc.

[0072] The model building unit (330) can build an artificial intelligence (AI) model that learns growth data and cultivation environment data divided into above-ground and underground parts together with chlorophyll fluorescence analysis data related to strawberries to predict the stress index of strawberries according to the growth stages and cultivation environment conditions of each above-ground and underground part. The model building unit (330) can perform the model building process by objectifying the correlation between each data through matched data of chlorophyll fluorescence analysis data, strawberry growth data (including, for example, growth surveys and growth images), and cultivation environment data and using them as learning data. At this time, the artificial intelligence model may correspond to a model that has learned to predict the degree of stress received by strawberries through the growth stages divided into above-ground and underground parts or cultivation environment conditions based on the chlorophyll fluorescence analysis data of strawberries.

[0073] In other words, the AI ​​model can receive as input a feature vector composed of a combination of growth data and cultivation environment data, and produce as output a vector relating to the strawberry's stress index. If the growth or cultivation environment data are collected separately for above-ground and below-ground parts, the AI ​​model can predict the strawberry's stress index using only the data from above-ground or below-ground parts, based on the learned input data.

[0074] For example, training data for an artificial intelligence model can be expressed as a feature vector of a specific size and can be composed of pairs of input data and output data. The input vector can be composed of feature information extracted from growth data and cultivation environment data, and can include feature information classified into above-ground and underground parts, if necessary. In this case, the input vector can be generated as a result of applying independent weights to the above-ground and underground parts. Furthermore, the input vector can be generated through an operation between embedding vectors generated as a result of aligning the feature information of each above-ground and underground part in the same vector space. The distance (or similarity) between the vectors generated through the embedding technique can be closer as the growth stage or cultivation environment conditions are similar, and the same cluster group can be formed based on the classification result of the above-ground or underground part.

[0075] To build an artificial intelligence model, structured time-series input data can be converted into feature vectors using Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTMs). Unstructured data, such as images, can be featured using Convolutional Neural Networks (CNNs). At this time, the feature information extracted from each data can be interconnected according to the structure of the artificial intelligence model and trained as input data.

[0076] In one embodiment, the model building unit (330) can perform data analysis through growth modeling using plant growth data, as well as basic simple correlation analysis and multiple correlation analysis between chlorophyll fluorescence analysis data, growth data, and cultivation environment data related to strawberries. For example, the model building unit (330) can track changes in strawberry growth data over time through growth modeling, and can utilize the results in the stress index calculation process after matching them with various numerical values ​​obtained from chlorophyll fluorescence analysis.

[0077] In one embodiment, the model building unit (330) may build an artificial intelligence model by learning at least a portion of a dataset including chlorophyll fluorescence analysis data, a first combination between above-ground growth data and below-ground growth data, and a second combination between first cultivation environment data and second cultivation environment data. At this time, the artificial intelligence model may be trained to predict and output stress factors among strawberry growth stages and cultivation environment conditions along with a stress index according to the data composition of the dataset used in the model learning process.

[0078] For example, a dataset used to build an artificial intelligence model may include training data of various data compositions. The dataset may be composed of only chlorophyll fluorescence analysis data and data classified as aboveground or underground, may be composed of chlorophyll fluorescence analysis data and growth data or cultivation environment data, or may be composed of all data. Furthermore, the inputs and outputs of the artificial intelligence model may vary depending on the data composition of the training data. That is, the stress index of a strawberry may be predicted as an output by receiving at least one of the growth data and cultivation environment data as input, or the stress index of a strawberry may be predicted as an output by receiving only the aboveground or underground data among the growth data and cultivation environment data as input.

[0079] As another example, an AI model may receive growth data and cultivation environment data as inputs, and predict and output the growth stage of the strawberry currently being cultivated along with the strawberry's stress index. Furthermore, the AI ​​model may receive growth data and cultivation environment data as inputs, and predict and output stress factors among the stress index, growth stage, and cultivation environment conditions. At this time, the stress factors may correspond to multiple cultivation environment conditions, and the multiple cultivation environment conditions may be sorted according to priority. Accordingly, the stress index calculation device (130) may effectively reduce the stress of the strawberry by predicting the strawberry's stress index and then actively controlling the cultivation environment conditions that become the stress factors.

[0080] The stress index prediction unit (350) can predict the stress index according to the growth stage and cultivation environment conditions of the strawberry currently being cultivated using an artificial intelligence model. For example, the growth stages of a strawberry can progress through the processes of runner generation, plant development, flower bud formation, dormancy, and flowering and fruiting. The stress index prediction unit (350) can generate the stress index according to the current growth stage and cultivation environment conditions of the strawberry as an objective quantitative index through an artificial intelligence model. For example, the stress index prediction unit (350) can predict Fm, Fv, Fo, Ft, Fq, Qmax, Fv / Fm, NPQ, qN, qP, qL, Rfd values, etc. as the stress index of the strawberry, and can also predict the function value of a functional equation defined by a combination of the corresponding indices.

[0081] In particular, the stress index prediction unit (350) can input the current growth stage and cultivation environment conditions into above-ground and underground parts respectively into the artificial intelligence model, thereby more accurately predicting the stress index of strawberries. If the data for above-ground or underground parts among the growth stage and cultivation environment conditions does not exist or is insufficient, the stress index prediction unit (350) can estimate and supplement the remaining data with the data for the underground or above-ground parts, and input the data into the artificial intelligence model to predict the stress index of strawberries.

[0082] The cultivation environment control unit (370) can actively control the cultivation environment of strawberries currently being cultivated based on the stress index. Here, the cultivation environment of strawberries can include temperature, humidity, VPD, light quantity, and light spectrum as above-ground environmental conditions, and can include nutrient solution used in hydroponics (i.e., ratio and concentration of ionized nutrients, etc.), EC, pH, nutrient solution temperature, and irrigation cycle as underground environmental conditions. That is, the stress index predicted by the stress index prediction unit (350) can be used for plant stress management for hydroponic cultivation of strawberries.

[0083] For example, the cultivation environment control unit (370) can use the stress index as an indicator of strawberry root stress, which is essential in hydroponic cultivation, and can be used to control a stress management environment suitable for root establishment and early growth, in particular. As another example, the cultivation environment control unit (370) can selectively control the above-ground or underground part of the strawberry according to the stress index, thereby controlling the cultivation environment.

[0084] In one embodiment, the cultivation environment control unit (370) can control the cultivation environment to alleviate the overall stress of the strawberry during the root establishment and growth period based on the stress index (e.g., control both the above-ground and underground parts) or to intensify a specific stress of the strawberry during the flower bud differentiation period (e.g., focus on controlling the above-ground parts). The cultivation environment control unit (370) can actively perform cultivation management through an objective and quantifiable strawberry stress index. For example, the cultivation environment control unit (370) can actively control the environment according to the growth stage of the strawberry by controlling the environment to conditions that can alleviate stress during the root establishment and growth period, which is advantageous when stress is low, and by intensifying a specific stress during periods such as flower bud differentiation, which may require a specific stress.

[0085] The control unit (not shown in FIG. 3) controls the overall operation of the stress index calculation device (130) and can manage the control flow or data flow between the data collection unit (310), the model construction unit (330), the stress index prediction unit (350), and the cultivation environment control unit (370).

[0086]

[0087] Figure 4 is a flowchart explaining a method for calculating a stress index of strawberries using chlorophyll fluorescence values ​​according to the present invention.

[0088] Referring to FIG. 4, the stress index calculation device (130) can collect chlorophyll fluorescence analysis data including a fluorescence analysis image for strawberries by linking with a chlorophyll fluorescence analysis device (110) through a data collection unit (310) (step S410). The stress index calculation device (130) can collect growth data of strawberries including a growth image of strawberries and matching the chlorophyll fluorescence analysis data through the data collection unit (310) (step S420). The stress index calculation device (130) can collect cultivation environment data of each above-ground and underground part during the growth process of strawberries through the data collection unit (310) (step S430).

[0089] That is, the stress index calculation device (130) can collect data during the cultivation of plants in various variation states by changing environmental conditions such as temperature, humidity, light quantity, and light quality in a plant cultivation system (e.g., vertical farm, etc.) that can collect data through precise environmental control and sensors, and can collect phenotype data through images during the process.

[0090] In addition, the stress index calculation device (130) can collect data of chlorophyll fluorescence measurement that can be matched with the collected data. At this time, the chlorophyll fluorescence measurement data can be collected in two forms. For example, the chlorophyll fluorescence measurement data can include numeric structured data expressed as an F value and two-dimensional unstructured data imaged according to the irradiation wavelength. The stress index calculation device (130) can standardize the structured data, environmental data and F value, by applying a weight, and can preprocess the data by extracting feature values ​​through CNN for the unstructured data, phenotype image data and chlorophyll fluorescence image data.

[0091] In addition, the stress index calculation device (130) can build an artificial intelligence (AI) model that predicts the stress index of strawberries by learning at least one of growth data and cultivation environment data together with chlorophyll fluorescence analysis data related to strawberries through the model construction unit (330) (step S440). Meanwhile, the stress index calculation device (130) can build an artificial intelligence model by applying various model structures.

[0092] In addition, the stress index calculation device (130) can predict the stress index according to the growth stage of the strawberry by using an artificial intelligence model through the stress index prediction unit (350) (step S450). The stress index calculation device (130) can predict the stress index of the strawberry under various conditions by constructing an artificial intelligence model with various model structures. For example, the stress index calculation device (130) can estimate the current strawberry stress based on the collected growth data, or use the artificial intelligence model to find the current stress factor based on the chlorophyll fluorescence analysis data.

[0093] In addition, the stress index calculation device (130) can not only measure the current stress level and causal factors through an artificial intelligence model, but can also simulate the predicted values ​​when the pattern and conditions change over time as cultivation time passes. Meanwhile, the stress index calculation device (130) can equally apply the stress index calculation method through an artificial intelligence model to correlation analysis techniques or growth modeling techniques, thereby performing cultivation management.

[0094]

[0095] FIG. 5 is a drawing illustrating one embodiment of a fluorescence analysis image of a strawberry according to the present invention.

[0096] Referring to FIG. 5, the stress index calculation device (130) can collect chlorophyll fluorescence analysis data from fluorescence analysis images of true leaves of strawberries. To this end, the stress index calculation device (130) can operate in conjunction with a chlorophyll fluorescence analysis device (110).

[0097] Here, photosynthesis can be defined as the process by which plants convert light energy into chemical energy, and various chemical and physical mechanisms and factors can be involved in this energy conversion process. For example, light energy absorbed by chlorophyll can be concentrated in reaction centers to perform useful chemical work, and among these, it can be released as heat or fluorescence during the electron transfer process of plastoquinone (PQ) in photosystem II. Therefore, changes in chlorophyll fluorescence can reflect changes in heat release and photosynthesis.

[0098] Additionally, when chlorophyll is briefly illuminated with saturating light, chlorophyll fluorescence can increase from Fo to maximum fluorescence (Fm), and under these conditions, Q, the first electron acceptor of photosystem II, A When photosystem II is fully reduced, the maximum quantum yield (Fv / Fm = (Fm-Fo) / Fm) of photosystem II can be measured. In this case, the Fv / Fm value of the leaves of healthy higher plants can always be maintained at around 0.8. A value lower than this may indicate damage to the reaction center of the photosystem or a stressful environment, and this can be used to non-destructively measure the objective stress level during the plant cultivation process.

[0099] That is, the stress index calculation device (130) can collect various chlorophyll fluorescence parameters as chlorophyll fluorescence analysis data from the fluorescence analysis image of the true leaf of the strawberry. For example, the chlorophyll fluorescence parameters may correspond to values ​​expressed as quantified values ​​of chlorophyll fluorescence analysis, and Fm, F V , F O , FT , Fq, Qmax, F V / F m , NPQ, qN, qP, qL, Rfd values, etc. may be included.

[0100] Additionally, the stress index calculation device (130) may use a functional formula through a combination of multiple chlorophyll fluorescence parameters. For example, the chlorophyll fluorescence variable (VariableChlfluorescence) Vv=F T -F O and Maximum variable chlorophyll fluorescence F V =F m -F O , and the relative variable chlorophyll fluorescence (Relative variable Chlfluorescence) V T =(F T F O ) / (F m -F O ) and the maximum photochemical activity rate (MaximumphotochemicalactivityofPSII) F V / F m =(1-F O / F m ) is. Here, F m is the maximum fluorescence, and F O is the ground fluorescence, F T is the steady state value

[0101] In addition, the stress index calculation device (130) can use RC / ABS, etc. associated with multiple chlorophyll fluorescence parameters as parameters. RC / ABS is Q B Photosystem II antenna chlorophyll Q for reduction Ais the number of reduction reaction centers. At this time, ABS = TR + DI, and ABS is the rate of photon absorption by the entire photosystem II antenna. TR is the rate of exciton trapping by the photosystem II reaction center, and DI is the rate of energy dissipation of all photosystem II in processes other than capture. In addition, the stress index calculation device (130) can use the main photosystem II photochemical maximum quantum yield as a parameter, and ' PO =TR O / ABS=1-(F O / F m )=F V / F m =PSII' can be expressed as follows.

[0102] The stress index calculation device (130) can collect chlorophyll fluorescence analysis data including fluorescence analysis images of strawberries according to the growth stage of the strawberries and collect growth data of the strawberries that matches the chlorophyll fluorescence analysis data. At this time, the chlorophyll fluorescence analysis data and the growth data can be matched based on the image data.

[0103]

[0104] Figure 6 is a drawing illustrating one embodiment of an artificial intelligence model according to the present invention.

[0105] Referring to FIG. 6, the stress index calculation device (130) can construct a chlorophyll fluorescence AI model (630) through modeling using an artificial neural network for complex system analysis. The chlorophyll fluorescence AI model (630) can derive a stress index as an objective quantitative index by learning the correlation between chlorophyll fluorescence analysis data, growth data, and cultivation environment data collected according to the growth stage of strawberries. At this time, the chlorophyll fluorescence AI model (630) can be constructed in various structures, and can be used to predict data values ​​based on the correlation between data or to predict a stress index according to the growth stage depending on the constructed model structure.

[0106] In the case of FIG. 6, growth data (610) and cultivation environment data (620) collected during the data collection process can be provided as input data to the chlorophyll fluorescence AI model (630), and the chlorophyll fluorescence AI model (630) can analyze the correlation between them to generate a stress index (640) of the strawberry at the corresponding growth stage as output. At this time, the input data of the chlorophyll fluorescence AI model (630) can be defined in vector form, and the input vector of the chlorophyll fluorescence AI model (630) can be generated based on the feature vectors extracted from each data. For example, the first and second feature vectors extracted from the growth data (610) and cultivation environment data (620) can be sequentially connected to generate an input vector of the chlorophyll fluorescence AI model (630).

[0107] In addition, the chlorophyll fluorescence AI model (630) can output the stress index (640) of the strawberry or output chlorophyll fluorescence parameters as quantitative indicators of the stress index (640). The stress index calculation device (130) can use the chlorophyll fluorescence parameters output by the chlorophyll fluorescence AI model (630) to finally calculate the stress index (640) of the strawberry, and can adjust the cultivation environment for stress management according to the calculated stress index (640). To this end, the stress index calculation device (130) can be implemented to be linked with an automated strawberry cultivation system, and can directly control the environmental control and sensors of the strawberry cultivation system for stress management.

[0108]

[0109] Although the present invention has been described above with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various modifications and changes may be made to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below.

[0110]

[0111] [Explanation of symbols]

[0112] 100: Cultivation Management System

[0113] 110: Chlorophyll fluorescence analysis device 130: Stress index calculation device

[0114] 150: Database

[0115] 210: Processor 230: Memory

[0116] 250: User input / output section 270: Network input / output section

[0117] 310: Data Collection Department 330: Model Building Department

[0118] 350: Stress Index Prediction Unit 370: Cultivation Environment Control Unit

Claims

1. A step of collecting chlorophyll fluorescence analysis data including a fluorescence analysis image of a strawberry through a data collection unit and in conjunction with a chlorophyll fluorescence analysis device; A step of collecting growth data on the growth stages of the strawberry, which includes a growth image of the strawberry and matches the chlorophyll fluorescence analysis data, by dividing the data into above-ground and underground parts, through the above-mentioned data collection unit; A step of collecting cultivation environment data regarding the cultivation environment conditions of each of the above-ground and underground parts during the growth process of the strawberry through the above-mentioned data collection unit; A step of building an artificial intelligence (AI) model that predicts the stress index of the strawberry according to the growth stage and cultivation environment conditions of each of the above-ground and underground parts by learning the growth data and cultivation environment data divided into the above-ground and underground parts together with the chlorophyll fluorescence analysis data associated with the strawberry through the model building unit; and A method for calculating a stress index of a strawberry using chlorophyll fluorescence values, comprising: a step of predicting a stress index according to the growth stage and cultivation environment conditions of a strawberry currently being cultivated using the artificial intelligence model through a stress index prediction unit; 2. In the first paragraph, the step of collecting the chlorophyll fluorescence analysis data is A method for calculating a stress index of a strawberry using chlorophyll fluorescence values, characterized in that it comprises a step of extracting a plurality of chlorophyll fluorescence parameters from the fluorescence analysis image of the true leaf of the strawberry.

3. In paragraph 1, The step of collecting the above growth data is It includes a step of collecting above-ground growth data including the fresh weight, leaf area, leaf length, leaf width and number of leaves of the strawberry based on the above-ground part, and underground growth data including the fresh weight, length and diameter of the main root and ratio of fine roots of the strawberry based on the underground part. The step of collecting the above cultivation environment data is A method for calculating a stress index of strawberries using chlorophyll fluorescence values, characterized by comprising a step of collecting first cultivation environment data including temperature, humidity, VPD, light quantity and light spectrum as cultivation environment conditions of the above-ground part, and second cultivation environment data including the ratio and concentration of nutrient solution, EC, pH, temperature of the nutrient solution and irrigation cycle as cultivation environment conditions of the underground part.

4. In the third paragraph, the step of building the artificial intelligence model is A step of building the artificial intelligence model by learning at least a portion of a dataset including the chlorophyll fluorescence analysis data, a first combination between the above-ground growth data and the underground growth data, and a second combination between the first cultivation environment data and the second cultivation environment data, A method for calculating a stress index of a strawberry using chlorophyll fluorescence values, characterized in that the artificial intelligence model is trained to predict and output stress factors among the growth stages and cultivation environment conditions of the strawberry along with the stress index according to the data composition of the dataset used in the learning process.

5. A data collection unit that collects chlorophyll fluorescence analysis data including a fluorescence analysis image of a strawberry in conjunction with a chlorophyll fluorescence analysis device, growth data regarding the growth stages of each above-ground and underground part of the strawberry including a growth image of the strawberry and matching the chlorophyll fluorescence analysis data, and cultivation environment data regarding cultivation environment conditions of each above-ground and underground part during the growth process of the strawberry; A model construction unit that learns growth data and cultivation environment data divided into the above-ground and underground parts together with chlorophyll fluorescence analysis data associated with the above-mentioned strawberry to construct an artificial intelligence (AI) model that predicts the stress index of the above-mentioned strawberry according to the growth stage and cultivation environment conditions of each above-mentioned and underground part; A stress index prediction unit that predicts the stress index according to the growth stage and cultivation environment conditions of the currently cultivated strawberry using the above artificial intelligence model; and A strawberry cultivation management system using a stress index, including a cultivation environment control unit that actively controls the cultivation environment of the currently cultivated strawberries based on the stress index.

6. In paragraph 5, the cultivation environment control unit A strawberry cultivation management system using a stress index, characterized in that the cultivation environment is controlled in a direction to alleviate the overall stress of the strawberry during the root establishment and growth period of the strawberry based on the stress index, or the cultivation environment is controlled in a direction to intensify the specific stress of the strawberry during the flower differentiation period of the strawberry.

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