JP2023067615A - Mushroom toxicity determination program - Google Patents

Abstract

To accurately and automatically estimate presence of toxicity without human resources.SOLUTION: A mushroom toxicity determination program for determining whether a mushroom is toxic causes a computer to perform: an information acquisition step of acquiring image information of a mushroom as a determination target; and a determination step of determining whether the mushroom is toxic on the basis of reference image information according to image information acquired by the information acquisition step by using reference image information of the mushroom acquired in the past and a learned model related to presence or absence of the toxicity with a defined connection degree and using the reference image information as input and the presence or absence of the toxicity as output.SELECTED DRAWING: Figure 4

Description

The present invention relates to a mushroom toxicity determination program for determining mushroom toxicity.

Conventionally, mushrooms have been collected from mountainous areas and forests for food. There are poisonous mushrooms among mushrooms. If you accidentally pick up a poisonous mushroom and eat it, in the worst case you may die. Therefore, when collecting mushrooms, it is necessary to carefully determine whether they are poisonous mushrooms or not.

However, even experts often make mistakes in identifying such poisonous mushrooms, making it extremely difficult for inexperienced amateurs. For this reason, a system for discriminating poisonous mushrooms with high accuracy has long been desired, but the current situation is that no system has been devised yet.

Therefore, the present invention has been devised in view of the above-mentioned problems, and the object thereof is to detect mushroom toxicity automatically and with high accuracy without relying on human labor. To provide a toxicity determination program.

A mushroom toxicity determination program to which the present invention is applied is a mushroom toxicity determination program for determining mushroom toxicity, which includes an information acquisition step of acquiring image information of an image of a mushroom to be determined; The degree of association between the reference image information and the presence or absence of toxicity is defined, the input is the reference image information, and the output is the presence or absence of toxicity. and a determination step of determining the presence or absence of toxicity based on the corresponding reference image information.

Even without special skills or experience, anyone can easily identify poisonous mushrooms automatically and with high accuracy.

1 is a block diagram showing the overall configuration of a system to which the present invention is applied; FIG. It is a figure which shows the specific structural example of a search apparatus. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention.

Hereinafter, a mushroom toxicity determination program to which the present invention is applied will be described in detail with reference to the drawings.

First Embodiment FIG. 1 is a block diagram showing the overall configuration of a mushroom toxicity determination system 1 in which a mushroom toxicity determination program to which the present invention is applied is implemented. A mushroom toxicity determination system 1 includes an information acquisition unit 9 , an estimation device 2 connected to the information acquisition unit 9 , and a database 3 connected to the estimation device 2 .

The information acquisition unit 9 is a device for a person using this system to input various commands and information. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an imaging device capable of capturing an image, such as a camera. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper document. Further, the information acquisition unit 9 may be integrated with the estimation device 2 to be described later. The information acquisition unit 9 outputs the detected information to the estimation device 2 . Further, the information acquisition unit 9 may be configured by means for specifying position information by scanning map information. Further, the information acquisition unit 9 may be configured with an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. The information acquisition unit 9 may also be configured with a communication interface that acquires weather data from the Meteorological Agency or a private weather forecast company. The information acquisition unit 9 may be composed of a body sensor worn on the body to detect body data, and the body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be configured with a sensor for The information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading from a database. The information acquisition unit 9 may be configured by an odor sensor that detects odors and scents in addition to these.

The database 3 accumulates various information necessary for judging the toxicity of mushrooms.

That is, in the database 3, any one or more of the reference information described later and the presence or absence of toxicity are associated with each other and stored. Instead of storing such learning data in the database 3, the learning data may be stored in the discriminating device 2 or the information acquisition unit 9 side. That is, the discriminating device 2 and the information acquisition unit 9 may be so-called edge devices, and the edge devices may be equipped with artificial intelligence to accumulate learning data and perform inference.

The estimating device 2 is composed of an electronic device such as a personal computer (PC), for example, but in addition to the PC, it is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc. It may be one that is made into. A user can obtain a search solution by this estimation device 2 .

FIG. 2 shows a specific configuration example of the estimation device 2 . The estimating device 2 performs wired or wireless communication with a control unit 24 for controlling the entire estimating device 2 and an operating unit 25 for inputting various control commands via operation buttons, a keyboard, or the like. A communication unit 26 for searching, an estimating unit 27 for making various judgments, and a storage unit 28, represented by a hard disk, for storing a program for performing a search to be executed are connected to the internal bus 21, respectively. . Further, the internal bus 21 is connected with a display unit 23 as a monitor for actually displaying information.

The control unit 24 is a so-called central control unit for controlling each component implemented in the estimating device 2 by transmitting control signals via the internal bus 21 . In addition, the control unit 24 transmits commands for various controls through the internal bus 21 according to operations through the operation unit 25 .

The operation unit 25 is embodied by a keyboard or a touch panel, and a user inputs an execution command for executing a program. The operation unit 25 notifies the control unit 24 when the execution command is input by the user. Upon receiving this notification, the control unit 24 cooperates with each component including the estimation unit 27 to execute desired processing operations. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates a search solution. The estimation unit 27 reads various information stored in the storage unit 28 and various information stored in the database 3 as necessary information in executing the estimation operation. This estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any known artificial intelligence technology.

The display unit 23 is composed of a graphic controller that creates a display image based on control by the control unit 24 . The display unit 23 is implemented by, for example, a liquid crystal display (LCD).

When the storage unit 28 is configured with a hard disk, predetermined information is written to each address based on the control by the control unit 24, and this information is read as necessary. The storage unit 28 also stores a program for executing the present invention. This program is read and executed by the control unit 24 .

The operation of the mushroom toxicity determination system 1 configured as described above will be described.

The toxicity determination system 1 for mushrooms inspects the presence or absence of such toxicity. The presence or absence of toxicity as used herein may indicate not only whether the mushroom is toxic, but also the degree of toxicity of the mushroom. Also, the types of mushrooms to be identified include all types of mushrooms.

As shown in FIG. 3, for example, the mushroom toxicity determination system 1 is based on the premise that three or more degrees of association between the reference appearance information and the presence or absence of toxicity are set in advance. Appearance information for reference includes the shape of the cap of the mushroom extracted by capturing an image of the appearance of the mushroom acquired in the past, the color of the surface of the cap, the form of the back of the cap, the color of the back of the cap, The information consists of one or more of the shape of the stalk of the mushroom and the color of the stalk, but the information is not limited to these, and may consist of any other information relating to the appearance of the mushroom. Another example of the appearance of the mushroom may be the shape and spacing of folds formed on the back surface of the mushroom cap. Such reference appearance information is assumed to be extracted by capturing an image of the mushroom appearance. In such a case, the various factors described above may be extracted by image analysis of an image of the appearance of the mushroom. In such a case, determination may be made based on previously learned feature amounts. At this time, using artificial intelligence, image data, the shape of the mushroom cap, the color of the front surface of the umbrella, the form of the back surface of the umbrella, the color of the back surface of the umbrella, the shape of the handle of the mushroom, and the shape of the handle. Any of the colors is learned, and when actually acquiring reference appearance information, these learned data are used to obtain the shape of the mushroom cap, the color of the surface of the cap, and the cap from the image data. The appearance information for reference may be obtained by extracting any one of the shape of the back surface of the mushroom, the color of the back surface of the umbrella, the shape of the handle of the mushroom, and the color of the handle.

In such a case, any of the image data of the mushroom, the shape of the mushroom cap, the color of the surface of the cap, the form of the back of the cap, the color of the back of the cap, the shape of the handle of the mushroom, and the color of the handle Using a predictive model machine-learned with the heel as training data, the input is image data, and the output is the shape of the mushroom cap, the color of the surface of the umbrella, the shape of the back of the umbrella, the color of the back of the umbrella, the Either the shape of the mushroom pattern or the color of the pattern above.

The appearance of a mushroom is obtained from the appearance obtained by imaging the mushroom, and can be obtained by analyzing the appearance information. The reference appearance information includes any information as long as it is extracted from the image that constitutes the appearance of the mushroom. good. This image may be a moving image as well as a still image. Also, although this image is composed of visible light, it may be composed of a so-called spectrum image in which the display color is switched according to the spectrum instead of the image. This reference appearance information may specify the appearance of a mushroom based on any of the size, shape, and color of the mushroom by analyzing the captured image of the mushroom. Also, this reference appearance information may be composed of an ultrasonic image of the mushroom.

A data set consisting of such reference appearance information and the presence or absence of toxicity is obtained and used for learning.

In the example of FIG. 3, it is assumed that the input data are, for example, reference appearance information P01 to P03. Such reference appearance information P01 to P03 as input data are linked to the presence or absence of toxicity as output. In this output, the presence or absence of toxicity is displayed as an output solution.

The reference appearance information is associated with each other through three or more degrees of association with the presence or absence of toxicity A to D as the output solution. Assuming that the mushrooms are of the same type, the presence or absence of toxicity may be indicated by the presence or absence of toxicity, for example, presence or absence of toxicity A indicates toxicity, and presence or absence of toxicity B indicates no toxicity. In addition, the presence or absence of toxicity C may be indicated by the degree of toxicity, such as toxicity 〇〇%, and the presence or absence of toxicity D may be indicated by the degree of toxicity, such as toxicity ▲▲%. Appearance information for reference is arranged on the left side through this degree of association, and the presence or absence of each toxicity is arranged on the right side through the degree of association. The degree of association indicates the degree of the presence or absence of toxicity and the degree of high association with respect to the reference appearance information arranged on the left side. In other words, this degree of association is an index that indicates whether each reference appearance information is likely to be associated with the presence or absence of toxicity, and is used to select the most probable presence or absence of toxicity from the reference appearance information. It shows the accuracy in In the example of FIG. 3, w13 to w19 are shown as association degrees. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of correlation between each combination as an intermediate node and the presence or absence of toxicity as an output. Conversely, the closer to one point, the lower the degree of correlation between each combination as an intermediate node and the price as an output.

The estimating device 2 acquires in advance the degrees of association w13 to w19 of three or more stages shown in FIG. That is, the estimating device 2 accumulates past data sets to determine which of the appearance information for reference and the presence or absence of toxicity in that case was adopted and evaluated in determining the actual search solution, and stores these data sets. By analyzing and analysing, the degree of association shown in FIG. 3 is created.

For example, assume that the reference appearance information is α. As for the presence or absence of toxicity for such reference appearance information, it is assumed that the presence or absence of toxicity A was highly evaluated. By collecting and analyzing such a data set, the degree of association with the reference appearance information is strengthened.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference appearance information P01, analysis is performed from various data obtained as a result of evaluation of the presence or absence of toxicity in the past. In the case of this reference appearance information P01, if there are many cases of the presence or absence of toxicity A, the degree of association leading to the evaluation of the presence or absence of toxicity is set higher, and if there are many cases of the presence or absence of toxicity B , set a higher degree of association leading to an assessment of the presence or absence of this toxicity. For example, in the example of the reference appearance information P01, the presence or absence of toxicity A and the presence or absence of toxicity C are linked. is set to 2 points.

In addition, as shown in FIG. 4, this degree of association may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data through a data set of images of the appearance of mushrooms to be evaluated in the past and the presence or absence of toxicity that was actually estimated and evaluated, the presence or absence of toxicity is actually newly determined from now on. Then, the presence or absence of toxicity is searched for using the learned data described above. Mushroom toxicity data for creating learning data may be obtained by, for example, receiving opinions from experts.

When newly performing toxicity discrimination, appearance information is newly acquired for the mushroom to be discriminated. Appearance information to be newly acquired is input by the information acquisition unit 9 described above. Appearance information is acquired by imaging mushrooms for which the presence or absence of toxicity is to be determined. The method of obtaining this appearance information from the image may be the same method as in the case of obtaining the reference appearance information described above.

Based on the appearance information newly acquired in this way, the presence or absence of toxicity is determined. In such a case, reference is made to the degree of association shown in FIG. 3 (Table 1), which has been acquired in advance. For example, when the newly acquired appearance information is the same as or similar to P02, the presence or absence of toxicity B is associated with w15, and the presence or absence of toxicity C is associated with the degree of association w16. In such a case, the presence/absence of toxicity B, which has the highest degree of correlation, is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the presence or absence of toxicity C, which has a low degree of association but the association itself can be recognized, may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association. It should be noted that the degree of association is not limited to the case of using three or more levels, but is composed of two levels of relevance, that is, 1 or 0, indicating whether or not there is a relationship. There may be. In such a case, a table in which reference appearance information on the input side and presence/absence of toxicity on the output side are associated with each other may be referred to. When certain appearance information is input, the presence or absence of toxicity associated with the corresponding reference appearance information is output.

In this way, it is possible to search for the presence or absence of the most suitable toxicity from the newly acquired appearance information, and to estimate and display it.

In FIG. 3, as an alternative to the external appearance information for reference, three or more degrees of association between the internal information for reference obtained by imaging the inside of an incised mushroom obtained in the past and the presence or absence of toxicity are learned in advance. may The incised inside is composed of image data obtained by photographing the inside of the mushroom that has been incised by tearing, splitting, tearing off, or cutting the stalk or cap portion of the mushroom with a camera. Three or more degrees of association between such reference internal information and the presence or absence of toxicity may be learned in advance. When acquiring this reference internal information, the folds, shape, color, etc. inside the dissected mushroom may be extracted by image analysis. In such a case, determination may be made based on previously learned feature amounts. At this time, artificial intelligence is used to learn the image data inside the mushroom and one of these factors, and when actually acquiring the internal information for reference, these learned data are used, The folds, shape, color, etc. of the mushroom may be extracted from the image data. At this time, as the image data, spectral images captured by a so-called spectral camera, in which colors are classified by wavelength, may be used.

By constructing the reference internal information obtained in this way and obtaining the actual presence or absence of toxicity for the photographed mushrooms, a data set is obtained in the same manner as described above, and the degree of association as shown in FIG. keep forming. Next, internal information is obtained by imaging the inside of the cut mushroom whose toxicity is to be estimated. This internal information acquisition method is the same as the reference internal information acquisition method described above. Next, the degree of association shown in FIG. 5 can be referenced to similarly search for the presence or absence of toxicity.

According to the present invention, three or more degrees of association between the reference appearance information and the presence or absence of toxicity are learned in advance, and the degree of association is used to search for the presence or absence of toxicity. . In other words, the appearance information and the presence or absence of toxicity may be based on not three or more degrees of association, but two or more degrees of association. Two or more levels of association may indicate whether or not each item of reference appearance information is associated with the presence or absence of toxicity.

For example, the appearance information for reference G11 is poisonous, the appearance information for reference G12 indicates that there is a 50% possibility of toxicity, and the appearance information for reference G13 indicates that there is no toxicity. The presence or absence of toxicity is linked on a one-to-one basis. It is also possible to prepare in advance data in the form of a template or table for such linkage. Then, when the appearance information corresponding to the reference appearance information is actually obtained, the template or table may be referred to, and the presence or absence of toxicity corresponding thereto may be output. That is, it is possible to acquire the presence or absence of toxicity based on the reference appearance information corresponding to the appearance information.

Similarly, it is not limited to the case where three or more degrees of association between the reference internal information and the presence or absence of toxicity are learned in advance, and the presence or absence of toxicity is searched for by using the degrees of association. In other words, the internal information and the presence/absence of toxicity should be based on not three or more degrees of association, but two or more degrees of association. Two or more levels of association may indicate whether or not each piece of reference surface information is associated with the presence or absence of toxicity.

In the example of FIG. 6, it is assumed that other reference information combinations of reference appearance information and other reference information are formed. Other information for reference includes internal information for reference, object information for reference, incision mode information for reference, timing information for reference, elasticity information for reference, and the like.

Reference target information and target information are information indicating the target on which the mushroom grows. Mushrooms usually grow on trees in many cases, and this indicates the type of tree on which they grow, the type of tree, the species, and the name. Information about the location or region where the tree grows may also be included. The reference target information may also include information indicating on which part of the tree (branch, root, trunk, leaf) the mushroom actually grows, if the mushroom actually grows on the tree. If it grows on a dead tree, the state of the dead tree, the type of the dead tree, and information about the surroundings of the dead tree may be included in the reference target information and the target information. In addition, since mushrooms sometimes grow from soil, the information about the soil components of the soil and the location or region where the soil is located may be included in this reference target information and target information. In obtaining such reference target information and target information, an image of a target on which mushrooms are growing may be captured by a camera and extracted by image analysis. In such a case, determination may be made based on previously learned feature amounts. At this time, using artificial intelligence, image data, the above-mentioned reference target information, and what constitutes the target information (type of tree, type of tree, part of tree, state of dead tree, type of dead tree, etc.) is learned, and when actually obtaining the reference target information and the target information, these learned data may be used to extract from the image data.

The reference incision manner information is information relating to the incision manner at the time of incising the mushroom. The method of incising the mushroom may be any method such as tearing, splitting, tearing off, or cutting the stalk or cap portion of the mushroom. When the mushroom is incised in this way, the shape and color of the incised portion, how it splits, and the like become reference incision mode information and incision mode information. The reference incision mode information and the incision mode information may be composed of, for example, an image of a fractured surface after incision. In such a case, determination may be made based on previously learned feature amounts. At this time, artificial intelligence is used to learn the image data and the characteristic portions of the fracture surface that constitute the reference target information described above, and the reference incision mode information and the incision mode information are actually acquired. In some cases, these learned data may be used for extraction from image data.

The timing information for reference and the timing information are information regarding the timing for making the determination. The timing for this determination may be broadly defined on a seasonal, monthly, or weekly basis, or may be subdivided into daily, hourly, minutely, secondly, or the like. For some mushrooms, the timing of identification may affect whether they are toxic or not, so this is included for reference.

The reference elasticity information and elasticity information are based on measured values of elasticity when the mushroom is pressed. The elasticity of the mushroom may be measured via an elasticity sensor for measuring the elasticity of the object. Since the resilience of mushrooms is also a factor that influences the type of mushrooms to be determined, the determination accuracy can be improved by combining these factors and determining the types of mushrooms through the degrees of association.

In the example of FIG. 6, it is assumed that the input data are, for example, reference appearance information P01 to P03 and other reference information P14 to P17. An intermediate node shown in FIG. 6 is obtained by combining the appearance information for reference as such input data with other information for reference. Each intermediate node is also connected to an output. In this output, the presence or absence of toxicity is displayed as an output solution.

Each combination (intermediate node) of reference appearance information and other reference information is associated with each other through three or more degrees of association with the presence or absence of toxicity as the output solution. Appearance information for reference and other reference information are arranged on the left side through this degree of association, and the presence or absence of toxicity is arranged on the right side through the degree of association. The degree of relevance indicates the degree of high relevance between the appearance information for reference and other reference information arranged on the left side and the presence or absence of toxicity. In other words, the degree of association is an index indicating whether each reference appearance information and other reference information is likely to be associated with the presence or absence of any toxicity, and the reference appearance information and other reference information It indicates the accuracy in selecting the most probable presence or absence of toxicity from the information. Therefore, the presence or absence of optimal toxicity is searched for by combining these reference appearance information and other reference information.

In the example of FIG. 6, w13 to w22 are shown as association degrees. These w13 to w22 are shown in 10 stages as shown in Table 1. The closer to 10 points, the higher the degree of correlation between each combination as an intermediate node and the output. The closer it is, the lower the degree of correlation between each combination as an intermediate node and the output.

The estimating device 2 acquires in advance the degrees of association w13 to w22 of three or more stages shown in FIG. In other words, the estimating device 2 accumulates past data as to which of the reference appearance information and the other reference information, and the presence or absence of toxicity in that case, is suitable for determining the actual search solution. Then, by analyzing these, the degree of association shown in FIG. 6 is created.

For example, it is assumed that the appearance information for reference in an actual case in the past is α. It is also assumed that the other reference information is time W, which is certain reference time information. In such a case, the presence or absence of toxicity, which indicates the degree of the actual presence or absence of toxicity, is learned as a data set and defined in the form of the degree of association described above. Such reference appearance information and other reference information may be extracted from a management database managed by a trader or the like.

This analysis may be performed by artificial intelligence. In such a case, for example, if the reference appearance information P01 is the other reference information P16, the presence or absence of toxicity thereof is analyzed from past data. If there are many cases with toxicity A, the degree of association leading to this soundness A is set higher, and if there are many cases with toxicity B and few cases with toxicity A, toxicity The degree of association leading to the presence/absence of toxicity B is set high, and the degree of association leading to the presence/absence of toxicity A is set low. For example, in the example of the intermediate node 61a, it is linked to the output of the presence or absence of toxicity A and the degree of soundness B, but from the previous example, the degree of association of w13, which leads to the presence or absence of toxicity A, is set to 7 points, and it is connected to the presence or absence of toxicity B. The degree of association of w14 is set to 2 points.

Further, the degree of association shown in FIG. 6 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence.

In the example of the degree of association shown in FIG. 6, the node 61b is a node of the combination of the external appearance information for reference P01 and the other information for reference P14. is w16. The node 61c is a node that is a combination of the reference position information P15 and P17 with respect to the reference appearance information P02. .

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually judging the presence or absence of toxicity. In such a case, the appearance information and the information corresponding to the reference information are actually acquired. For example, if the reference information is reference internal information, the information corresponding to the reference information corresponds to the internal information of the mushroom to be determined. If it is the reference target information, it corresponds to the target information of the mushroom to be determined. The reference incision state information corresponds to the incision state information of the mushroom to be discriminated. If it is time information for reference, it corresponds to the time information at the time of discrimination. The elasticity information for reference corresponds to the elasticity information of the mushroom to be determined.

Based on the appearance information newly acquired in this way and the information corresponding to the reference information, the presence or absence of optimal toxicity is searched for. In such a case, reference is made to the degrees of association shown in FIG. 6 (Table 1) that have been acquired in advance. For example, when the newly acquired appearance information is the same as or similar to P02 and the information corresponding to the reference information is P17, the node 61d is associated via the degree of association. This node 61d is associated with the presence/absence of toxicity C by w19 and the presence/absence of toxicity D by w20. In such a case, the presence/absence of toxicity C, which has the highest degree of correlation, is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the presence or absence of toxicity D, which has a low degree of association but the association itself can be recognized, may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association.

Table 2 below shows examples of degrees of association w1 to w12 extending from the input.

Intermediate nodes 61 may be selected based on degrees of association w1 to w12 extending from this input. In other words, the greater the degree of association w1 to w12, the heavier the weight in selecting the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weighting in selecting the intermediate nodes 61 may all be the same.

In the above-described example of FIG. 6, the case of applying the degree of association regarding the presence or absence of toxicity to a combination of reference appearance information and other reference information has been described as an example, but the present invention is not limited to this. Instead, it may form a degree of association regarding the presence or absence of toxicity with respect to a combination of the internal information for reference and other information for reference. Since the method for judging the toxicity of mushrooms with or without toxicity in such a case is the same as described above, the explanation below is omitted.

In the above-mentioned degree of relevance, the degree of relevance is expressed by a 10-level evaluation, but it is not limited to this, and it may be expressed by a degree of relevance of 3 or more stages. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are associated with each other, either 1 or 0.

According to the present invention configured as described above, anyone can easily search for the presence or absence of toxicity without special skills or experience. Further, according to the present invention, it is possible to judge the search solution with a higher degree of accuracy than a human being does. Furthermore, by configuring the degree of association described above with artificial intelligence (neural network, etc.) and making it learn, it is possible to further improve the discrimination accuracy.

Since the above-described input data and output data may not be exactly the same in many cases during the learning process, information obtained by classifying these input data and output data according to type may be used. That is, the information P01, P02, . . . P15, 16, . A data set may be created between the data and the output data for learning.

Incidentally, in the degree of association described above, instead of reference appearance information or reference internal information, reference image information may be learned with the presence or absence of toxicity of mushrooms. The image information for reference and the image information are the images of mushrooms themselves. A solution search can be similarly performed from the image itself without performing image analysis like the external information for reference or the internal information for reference.

In addition, in the present invention, the type of mushroom itself may be learned instead of whether the mushroom is poisonous or not as a search solution. That is, the type of mushroom is discriminated regardless of whether the mushroom is poisonous or not. In such a case, a similar solution search can be performed by learning a data set consisting of the above-described reference information and types of mushrooms. When creating learning data, the type of mushroom may be obtained by having an expert or a person skilled in the art appraise and discriminate.

In addition, the degree of association described above has been described by taking as an example a case where it is configured by combining any of the above-described reference information in addition to reference external information or reference internal information, but it is limited to this. not a thing In other words, the degree of association may be configured by a combination of two or more of the reference information described above in addition to the reference appearance information or the reference internal information. Further, the degree of association may be formed by adding one or more of the above-mentioned reference information in addition to the reference appearance information or the reference internal information, and adding other factors to this combination.

Moreover, according to the present invention, it is characterized in that the optimum solution search is performed through the degree of association set to three or more stages. The degree of association can be described by a numerical value of, for example, 0 to 100% in addition to the 10 stages described above, but is not limited to this, and can be described by a numerical value of 3 or more stages. may be configured.

By discriminating the presence or absence of the most probable toxicity based on the degree of association represented by such numerical values of three or more levels, in a situation where there are multiple candidates for the possibility of a search solution, It is also possible to search and display. If it is possible to display to the user in descending order of degree of association in this way, it is also possible to preferentially display more probable search solutions.

In addition to this, according to the present invention, it is possible to make a judgment without overlooking even a very low output discrimination result such as a correlation degree of 1%. Remind the user that even discrimination results with an extremely low degree of association are connected as slight signs, and that once in dozens or hundreds of times, the discrimination results may be useful. be able to.

Furthermore, according to the present invention, there is an advantage that a search policy can be determined by setting thresholds by performing a search based on three or more degrees of association. If the threshold value is lowered, even if the degree of association is 1%, it can be picked up without omission. be. On the other hand, if the threshold is set high, there is a high possibility that the optimal search solution can be detected with a high probability. Sometimes the solution is overlooked. It is possible to decide which one to place emphasis on based on the way of thinking of the user side and the system side, and it is possible to increase the degree of freedom in selecting such points to place emphasis on.

Furthermore, in the present invention, the degree of association described above may be updated. This update may reflect information provided via a public communication network such as the Internet, for example. In addition, when acquiring external appearance information for reference, surface information for reference, and other information for reference, and acquiring knowledge, information, and data regarding the presence or absence of toxicity to these, improvement measures, the degree of association is increased accordingly, or lower.

In other words, this update corresponds to learning in terms of artificial intelligence. Since new data is acquired and reflected in the learned data, it can be said that it is a learning act.

In addition to updating the degree of association based on information that can be obtained from the public communication network, the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts It may be updated manually or automatically. Artificial intelligence may be used in these update processes.

Also, the process of initially building a trained model and the above-described updating may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of loading and learning datasets of input and output data, learning is performed by loading information corresponding to the input data, and from there self-forming the degree of association related to the output data. You can let it run.

When the degree of association shown in FIG. 3 is composed of neural network nodes in artificial intelligence, reference appearance information is input as input data, the presence or absence of toxicity is output as output data, and the input node At least one or more hidden layers may be provided between and the output node, and machine learning may be performed.

Further, according to the present invention, as shown in FIG. 7, the presence or absence of toxicity is determined based on the degree of association between a combination of two or more types of information, ie, information for reference U and information for reference V. FIG. It is assumed that this reference appearance information U is reference appearance information or reference surface information, and reference information V is any other reference information.

At this time, as shown in FIG. 7, the output obtained for the reference information U is directly used as input data, and is associated with the output (presence or absence of toxicity) through an intermediate node 61 in combination with the reference information V. good too. For example, after providing an output solution for reference information U (reference appearance information) as shown in FIG. The output (presence or absence of toxicity) may be searched.

Moreover, the present invention is not limited to the above-described embodiments. For example, as shown in FIG. can be In such a case, solution search is performed based on three or more degrees of association with the presence or absence of toxicity according to newly acquired information. Any of the reference information described above can be applied as the basic reference information, other than the reference appearance information or the reference internal information described above.

In these cases, similarly, when information corresponding to reference information used as learning data is input, solution search is performed based on the above-described method.

The search solution obtained through the degree of association may be further modified or weighted based on other reference information.

The other reference information referred to here corresponds to any reference information other than the basic reference information when any of the reference information described above is used as the basic reference information.

For example, in certain reference incision mode information P14, which is one of other reference information, it is assumed that B has often been determined as the presence or absence of toxicity in the past. When the presence or absence of toxicity corresponding to such reference incision mode information P14 is newly acquired, the search solution B for the presence or absence of toxicity is weighted. It is set in advance to perform processing to connect to.

For example, the other reference information G is an analysis result that suggests a more toxic search solution C, and the reference information F is an analysis result that suggests a more toxic search solution D. Assume that there is After the setting with the reference information in this way, if the actually obtained information is the same as or similar to the reference information G, processing is performed to increase the weight of the search solution C for the presence or absence of toxicity. On the other hand, when the actually acquired information is the same as or similar to the reference information F, processing is performed to increase the weight of the search solution D for the presence or absence of toxicity. In other words, the degree of association itself leading to the presence or absence of toxicity may be controlled based on the reference information FH. Alternatively, the presence or absence of toxicity may be determined only by the degree of association described above, and then corrections may be added to the obtained search solution based on the reference information FH. In the latter case, it is necessary to reflect what is designed on the system side each time as to how to correct the presence or absence of toxicity as a search solution based on the reference information F to H and with what weight.

The other reference information is not limited to any one type, and the solution search may be performed based on two or more types of reference information. Similarly, in such a case, the discriminant pattern as a search solution obtained through the degree of association may be corrected to a higher value for a case that leads to the presence or absence of toxicity suggested by the reference information.

Similarly, as shown in FIG. 9, even in the case of forming the degree of association with the presence or absence of toxicity for a combination having reference information that is the keynote and other reference information, the reference information that is the keynote is , any reference information is applicable. Other reference information includes any reference information other than the underlying reference information.

At this time, if the basic reference information is the reference target information, the other reference information includes any other reference information.

Even in such a case, the degree of deterioration can be estimated by searching for solutions in the same manner. At this time, as shown in FIG. 8 described above, the degree of deterioration of the search solution obtained through the degree of association is corrected through other reference information (reference information F, G, H, etc.). can be

At this time, the degree of association may be learned by combining not only one piece of other reference information but also two or more pieces of information.

Further, as shown in FIG. 10, the degree of association may be formed between only the basic reference information and the presence or absence of toxicity. Any reference information can be applied as this underlying reference information. Description of the solution search method in FIG. 10 is omitted below by citing the description of FIG.

Second Embodiment In the second embodiment, the discriminating device 2, or the discriminating device 2 and the information acquisition unit 9, among wearable terminals, particularly glasses-type terminals and head-mounted displays (HMDs) are used. This HMD is mounted integrally or partially on the user's head or eyeglasses, and utilizes technology such as augmented reality (AR) or mixed reality (MR), based on various image information acquired. and a display unit that displays the information generated by the process in a transparent state. The user can visually recognize and understand the information to be displayed through the display unit that transparently displays the information on the HMD. As a result, the user can check the information generated based on the various types of acquired video information and various contents together while looking at the situation in front of the user.

Therefore, in the present invention, for example, appearance information and surface information are acquired via the information acquisition unit 9 mounted on this HMD. A solution search may be performed by the discriminating device 2 mounted in the HMD, and the obtained search solution (presence or absence of toxicity) may be displayed in a transparent state via the display unit.

In the second embodiment, the basic reference information such as the reference appearance information, the reference internal information, and the reference object information to be learned as learning data in the second embodiment is actually used in glasses such as HMDs. However, it is not limited to this, and may be captured by a normal digital camera, smart phone, or the like.

In addition, in actually obtaining the external appearance information for reference, the internal information for reference, and the target information for reference, it may be possible to associate which part of the mushroom is being imaged.

For example, in obtaining the reference appearance information, it is detected what part of the mushroom is viewed by an experienced appraiser or a person skilled in the art who evaluates the presence or absence of toxicity. Detect whether a veteran appraiser sees mainly the bottom part of the mushroom, or sees the obliquely upper part of the mushroom, or sees the side part of the mushroom. do.

For this detection, for example, a veteran appraiser is made to wear a spectacles-type terminal and actually approach and check the mushrooms. continues to capture an image in the viewing direction. Then, by analyzing the recorded image after the fact or by reproducing the image, the mushroom It is possible to detect which part of the is being visually recognized.

In such a case, as shown in FIG. 11, when moving images captured by the glasses-type terminal are arranged in chronological order, an image of a mushroom pattern (P1), an image of the reverse side of a mushroom cap (P2), an umbrella and an enlarged image (P3) of the back side of the umbrella and an image (P4) of the front side of the umbrella. The imaging target site information may be detected from the images obtained in time series in this manner. The imaging target site information here is information regarding what site of the mushroom is captured by the image captured by the glasses-type terminal. As shown in FIG. 11, this imaging target region information may consist of the name of the region actually being imaged, such as the handle, the back of the umbrella, the back of the umbrella, or the like. It may be represented by a symbol, a numerical value, a number, or the like. The imaging target site information may also include, for example, whether the imaging is an enlarged image or a reduced image, and information such as the imaging direction and angle of view at the time of imaging. In the case of target information and reference target information, the imaging target region information is tree trunks, dead trees, branches, leaves, and the like.

The acquisition of the imaging target region information may be performed manually by a human identifying the imaging target region each time, or may be obtained by using a well-known image analysis technique for the acquired image. Acquisition of this imaging target region information may be determined based on previously learned feature amounts. For example, the image of each part such as the handle of the mushroom, the back of the umbrella, the back of the umbrella, etc. may be determined by extracting them through artificial intelligence using deep learning technology. In such a case, a machine learning model is used in which the part of the mushroom included in the reference appearance information and the imaging target part information are used as teacher data, the input is the reference appearance information, and the imaging target part information is the output. . Then, imaging target region information is acquired based on the reference external appearance information newly imaged via the user terminal. In addition to this, acquisition of imaging target part information is performed by using the direction of the line of sight detected using the eye-tracking function installed in the HMD and glasses-type terminal, and the direction of the head detected using the acceleration sensor and the gyro sensor. The body part information to be imaged may be acquired through the orientation of the part, the movement of the user's hand using the operation device or the hand tracking function, and the like.

By recording the imaging target site information thus obtained in association with the above-described images P1 to P4, etc., it is possible to set which site of the mushroom each of the images P1 to P4 is an image of. can be obtained at By obtaining images P1 to P4 constituting such reference appearance information and imaging target site information from a veteran appraiser, it is possible for a veteran appraiser to obtain any site in any manner when actually inspecting a mushroom. It is also possible to acquire whether or not the images are being checked in order, whether or not the images are enlarged images, and the shooting environment such as the shooting angle. As for the reference surface information, it is possible to similarly acquire the imaging target site information and record it in association with it.

In the second embodiment, it is possible to improve the convenience during the actual solution search by utilizing the imaging target region information. When a user wearing an HMD implements the present invention after building the above-described learning data in advance, a search for a search solution (presence or absence of toxicity, etc.) is performed based on the method described in the first embodiment. do. At this time, the imaging target part information may be similarly obtained in the process of acquiring the imaged appearance information, internal information, and target information of the mushroom that the user wearing the HMD is visually recognizing. As a method of acquiring this imaging target part information from the appearance information and the surface information, it may be determined based on the feature amount learned as described above. , the image of each part of the mushroom such as the back of the umbrella may be determined by extracting it through artificial intelligence using deep learning technology. In such a case, a machine learning model is used in which the part of the mushroom included in the appearance information and the imaging target part information are used as teacher data, the input is the appearance information, and the imaging target part information is the output. Then, imaging target region information is acquired based on appearance information newly imaged via the user terminal. Imaging target site information can be similarly obtained from the surface information.

Hereinafter, the imaging target region information obtained from information including appearance information, internal information, etc., is also referred to as first imaging target region information, and is obtained from reference information including reference appearance information, reference surface information, etc. The imaging target region information is also referred to as second imaging target region information.

Acquiring such first imaging target region information each time in the process of acquiring appearance information and surface information has the excellent effects described below. It is possible to confirm each time whether the first imaging target region information in the imaged appearance information matches the second imaging target region information in the reference appearance information. When the first imaging target region information in the imaged appearance information and the second imaging target region information in the reference appearance information do not match, a warning can be displayed to the user via the display unit of the HMD.

For example, when the first imaging target part information of the imaged appearance information is "pattern" and the second imaging target part information of the reference appearance information for corresponding to this is "back of umbrella", the HMD This means that the part of the mushroom whose appearance information is to be photographed by the user who wears the Mushroom is photographing the wrong part. In such a case, it is possible to prompt the user to align the imaging target with the correct part by calling attention as described above. Further, while the second imaging target part information linked to the reference appearance information is for magnifying and visually confirming the back surface of the umbrella, the first imaging target part information linked to the appearance information In the case where the image of the back side of the same umbrella is not visually magnified, it is possible to similarly prompt the user to magnify and visually recognize the image.

In this way, the degree of matching between the first imaging target region information in the imaged appearance information and the second imaging target region information in the reference appearance information, or the imaging target region information in the imaging surface information and the reference surface information Via the degree of matching with the imaging target part information, it is possible to make various suggestions to the user wearing the HMD about the actual appearance information and surface information imaging method, or prompt various corrections. . At this time, the above-described AR or MR may be realized in which such suggestions and promotion of correction are displayed in a transmissive state through the display unit of the HMD or glasses-type terminal. It should be noted that any suggestion about the imaging method may be displayed as long as it is based on the first imaging target region information and the second imaging target region information.

In the example of FIG. 11 described above, a description has been given by taking as an example a case in which shooting targets are sequentially switched for a plurality of locations for mushrooms, but the present invention is not limited to this. Similarly, by acquiring and linking the imaging target region information even in the case of the other, it is possible to perform the above-described guidance to the user who actually acquires the appearance information and the like.

In addition, as shown in FIG. 12, the present invention uses three or more degrees of association between reference appearance information or reference internal information, reference target information, reference incision mode information, etc., and the presence or absence of toxicity of the mushroom. You may make it acquire beforehand for every site|part. In such a case, for each part of the mushroom such as the stalk and cap, independent reference appearance information or reference surface information, reference internal information, reference target information, reference incision mode information, etc., and toxicity information are provided. The degree of association with presence/absence is learned and stored.

Then, the association degree of the part of the mushroom corresponding to the acquired first imaging target part information is read out, and solution search is performed in the same manner as described above. If the first imaging target part information is the handle of a mushroom, an evaluation model consisting of the degree of association for the handle is selected, and the toxicity of the mushroom is determined. As a result, it is possible to select an evaluation model that is specialized for each part of the mushroom, and to perform more accurate toxicity determination of the mushroom.

In the present invention, as shown in FIG. 13, the degree of association may be constructed for each part of the mushroom as described above, and the degree of association may be classified for each other reference information. Other reference information here includes all reference information described in the first embodiment. Among these, when focusing on the reference type information, for example, as shown in FIG. are formed, and as other reference information B, the degree of association for each part of the mushroom, such as for the handle, for the front surface of the umbrella, for the back surface of the umbrella, etc., is formed. The degree of relevance for each part, which is classified for each reference information, is prepared. Then, when a new solution search is performed, the above-described imaging target region information is acquired, and other information is acquired. The other information to be acquired corresponds to the other reference information described above. If the other reference information is reference time information, the time information is acquired. An evaluation model classified for each part of the mushroom in the reference information corresponding to the acquired other information is selected, and the solution search is performed in the same manner. In the case of the example of FIG. 13 described above, when the acquired information is other information B (time information B), the mushrooms are classified by part of the other reference information B (reference time information B) according to this. Then, one of the evaluation models is selected and solution search is performed.

Note that the degree of association constructed for each part of the mushroom may be constructed for each piece of any other information for reference, other than the case where it is constructed for each piece of information for reference as described above. In such a case, similarly, information corresponding to the reference information is obtained, and the degree of association is selected according to the obtained information.

In the present invention, as shown in FIG. 14, three or more degrees of association between reference appearance information or reference internal information and the presence or absence of toxicity may be acquired in advance for each model selection information. The model selection information here means the appearance information corresponding to the reference appearance information, the internal information corresponding to the reference internal information, and the target information corresponding to the reference target information as the reference information described in the first embodiment. information, incision mode information corresponding to reference incision mode information, time information corresponding to reference time information, and elasticity information corresponding to reference elasticity information. Details of these pieces of information are omitted below by citing the description of the first embodiment. The model selection information is selected from any one of the appearance information, internal information, target information, incision mode information, time information, elasticity information, etc. described in the first embodiment, and the breakdown of the selected model selection information. Each (selection information 1, 2, 3, . and save it. If the model selection information is timing information, selection information 1 includes timing 〇〇, selection information 2 includes timing ▲▲, and selection information 3 includes timing ◇◇. there is

Then, at the time of estimation, model selection information for the mushroom to be estimated is acquired. Then, the association degree of any of the mushroom selection information 1, 2, 3, . If the model selection information is the selection information 2, an evaluation model composed of the degree of association for the selection information 2 is selected, and mushroom toxicity determination is performed. As a result, it is possible to select an evaluation model specialized for mushroom selection information, and perform more accurate estimation.

In addition, as shown in FIG. 15, the present invention constructs the degree of association for the selection information 1, 2, 3, . may Other reference information here includes all reference information described in the first embodiment. Among these, when focusing on the reference elasticity information, for example, as shown in FIG. As other reference information B, the degrees of relevance are formed for each of the selection information 1, 2, 3, . . . A degree of association classified for each piece of reference information is prepared. When a new solution search is performed, the model selection information described above is obtained, and information other than the model selection information is obtained. The other information to be acquired corresponds to the other reference information described above. If the other reference information is elasticity information for reference, elasticity information is acquired. An evaluation model classified for each of selection information 1, 2, 3, . In the case of the example of FIG. 15 described above, when the other information B (elasticity information B) is acquired, the selection information 1, 2, 3, .

The degree of association constructed for each piece of model selection information may be constructed for each piece of any other information for reference, in addition to the case where it is constructed for each piece of elasticity information for reference as described above. In such a case, similarly, information corresponding to the reference information is obtained, and the degree of association is selected according to the obtained information.

1 mushroom toxicity determination system 2 estimation device 21 internal bus 23 display unit 24 control unit 25 operation unit 26 communication unit 27 estimation unit 28 storage unit 61 node

Claims (11)

In a mushroom toxicity determination program for determining the toxicity of mushrooms,
an information acquisition step of acquiring image information of an image of a mushroom to be identified;
The degree of association between reference image information of mushrooms acquired in the past and the presence or absence of toxicity is defined, using a trained model in which the input is the reference image information and the output is the presence or absence of toxicity, the above information acquisition step A mushroom toxicity determination program for causing a computer to execute a determination step of determining the presence or absence of toxicity based on reference image information corresponding to acquired image information. In a mushroom toxicity determination program for determining the toxicity of mushrooms,
The shape of the mushroom cap extracted by capturing an image of the appearance of the mushroom to be identified, the color of the surface of the cap, the form of the back of the cap, the color of the back of the cap, the shape of the handle of the mushroom, the above an information acquisition step of acquiring appearance information consisting of one or more of the colors of the pattern;
The shape of the mushroom cap, the color of the surface of the cap, the form of the back of the cap, the color of the back of the cap, the shape of the handle of the mushroom, extracted by capturing an image of the appearance of the mushroom acquired in the past, Using a trained model in which the reference appearance information consisting of one or more of the above pattern colors and the degree of association with the presence or absence of toxicity are defined, the input is the reference appearance information, and the output is the presence or absence of toxicity, A mushroom toxicity determination program for causing a computer to execute a determination step of determining the presence or absence of toxicity based on reference appearance information corresponding to the appearance information acquired in the information acquisition step. In a mushroom toxicity determination program for determining the toxicity of mushrooms,
an information acquisition step of acquiring internal information obtained by imaging the inside of an incised mushroom to be identified;
The degree of association between reference internal information obtained by imaging the inside of an incised mushroom obtained in the past and the presence or absence of toxicity is defined, and a trained model is used in which the input is the reference internal information and the output is the presence or absence of toxicity. and a determination step of determining presence or absence of toxicity based on the reference internal information corresponding to the internal information acquired in the information acquisition step. In the information acquisition step, internal information obtained by capturing an image of the inside of the mushroom to be identified is acquired,
In the determination step, the degree of association between the reference appearance information and reference internal information obtained in the past obtained by imaging the inside of an incised mushroom and the presence or absence of toxicity is defined. The presence or absence of toxicity is determined by using a trained model with internal information and the presence or absence of toxicity as an output, and further based on reference internal information corresponding to the internal information acquired in the information acquisition step. The mushroom toxicity determination program according to claim 2. The information acquisition step acquires target information about a target on which a mushroom to be determined grows,
In the determination step, the degree of association between the reference appearance information and the previously acquired reference target information about the target on which the mushroom grows is defined, and the input is the reference appearance information and the reference target. information, and the presence or absence of toxicity is determined by using a trained model in which the output is the presence or absence of toxicity, and further based on the reference target information corresponding to the target information acquired in the information acquisition step. Item 3. The mushroom toxicity determination program according to item 2. The information acquisition step acquires incision mode information regarding the incision mode when the mushroom to be determined is incised,
In the determination step, the degree of association between the reference appearance information and the previously acquired reference dissection pattern information regarding the dissection pattern when incising a mushroom and the presence or absence of toxicity is defined. determining the presence or absence of toxicity based on reference incision mode information corresponding to the incision mode information acquired in the information acquisition step, using a trained model in which the output is the presence or absence of toxicity; The mushroom toxicity determination program according to claim 2, characterized by: The information acquisition step acquires time information regarding the time of collection of the mushroom to be identified,
In the determination step, the degree of association between the reference appearance information and the reference time information about the harvesting time of mushrooms acquired in the past and the presence or absence of toxicity is defined, and the input is the reference appearance information and the reference time information. and determining the presence or absence of toxicity based on reference timing information corresponding to the timing information obtained in the information obtaining step, using a trained model whose output is the presence or absence of toxicity. 2. The mushroom toxicity determination program described in 2 above. In the information acquisition step, elasticity information obtained by measuring the elasticity of the mushroom to be identified by an elasticity sensor is acquired,
In the determination step, the degree of association between the reference appearance information and the reference elasticity information obtained by measuring the elasticity of mushrooms obtained in the past by an elasticity sensor and the presence or absence of toxicity is defined. The presence or absence of toxicity is discriminated based on the elasticity information for reference and the elasticity information for reference corresponding to the elasticity information acquired in the information acquisition step, using a trained model with elasticity information for use and the presence or absence of toxicity as the output. The mushroom toxicity determination program according to claim 2. In the information acquisition step, internal information obtained by capturing an image of the inside of the mushroom to be identified is acquired,
3. The mushroom toxicity determination program according to claim 2, wherein, in the determination step, the presence or absence of toxicity is determined based on the internal information while using the learned model. 10. The mushroom toxicity determination program according to any one of claims 1 to 9, wherein the degree of association is composed of nodes of a neural network in artificial intelligence. In the mushroom type discrimination program for discriminating the types of mushrooms,
The shape of the mushroom cap extracted by capturing an image of the appearance of the mushroom to be identified, the color of the surface of the cap, the form of the back of the cap, the color of the back of the cap, the shape of the handle of the mushroom, the above an information acquisition step of acquiring appearance information consisting of one or more of the colors of the pattern;
The shape of the mushroom cap, the color of the surface of the cap, the form of the back of the cap, the color of the back of the cap, the shape of the handle of the mushroom, extracted by capturing an image of the appearance of the mushroom acquired in the past, Using a trained model in which the degree of association between the reference appearance information consisting of one or more of the pattern colors and the type of mushroom is defined, the input is the reference appearance information, and the output is the mushroom type, and a determination step of determining the type of mushroom based on the reference appearance information corresponding to the appearance information acquired in the information acquisition step.

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