JP6863153B2 - Dry weight estimation program, dry weight estimation method and dry weight estimation device - Google Patents

Description

The present invention relates to a dry weight estimation program, a dry weight estimation method and a dry weight estimation device.

In recent years, algae have been attracting attention as biomass, and algae are being cultivated in a culture tank in which a culture solution is stored. The biomass amount (biological amount) of algae is expressed by dry weight, for example, as dry weight per unit volume of culture solution. Generally, in the gravimetric analysis of algae, only the algae are extracted by filtering and drying the culture solution containing the algae, and the weight of the extracted algae is measured and the weight per unit volume (eg, g / L). It is converted to. The time required for gravimetric analysis is several hours including the drying time even if a dryer or the like is used.

By the way, in order to grasp the culture status of algae, it is indispensable to measure the dry weight regularly and continuously. Therefore, there is a technique of taking an image of a solution of algae, extracting a color component from the taken image, and estimating the concentration of algae from each color component.

Japanese Unexamined Patent Publication No. 2013-188157

However, the technique of estimating the algae concentration from the color component of the image of the algae solution may not be practical. In the commercialization of algae culture, it can be assumed that algae are cultivated on a large scale in a culture environment such as outdoors where the water temperature and light intensity are unstable. Extracting the numerical values of the color components from each image acquired from such a culture environment is a very complicated work and may not be practical.

In one aspect, it is an object of the present invention to provide a dry weight estimation program, a dry weight estimation method, and a dry weight estimation device that can easily estimate the dry weight of algae.

In one aspect, the dry weight estimation program takes a compressed image of the algae culture and causes the computer to perform a process of identifying the image volume of the compressed image. The dry weight estimation program refers to a storage unit that stores the relationship between the image capacity and the dry weight, and outputs an estimated value of the dry weight of the algae contained in the compressed image to the computer based on the specified image capacity. Let it run.

According to one embodiment, the dry weight of algae can be easily estimated.

FIG. 1 is a diagram showing an example of a configuration when culturing algae. FIG. 2 is a diagram showing an example of the configuration of the dry weight estimation device. FIG. 3A is a diagram schematically showing an image of a culture tank in a state where the concentration of algae is low. FIG. 3B is a diagram schematically showing an image of a culture tank in a state where the concentration of algae is high. FIG. 4A is a diagram showing an example of the data used for the regression analysis and the estimation result. FIG. 4B is a diagram showing a calculation result of regression statistics. FIG. 5 is a graph showing an example of the estimation result of the dry weight of algae for a predetermined period. FIG. 6 is a flowchart showing an example of the dry weight estimation process. FIG. 7 is a diagram showing an example of a hardware configuration.

Hereinafter, examples of the dry weight estimation program, the dry weight estimation method, and the dry weight estimation device disclosed in the present application will be described in detail with reference to the drawings. The disclosed technology is not limited by the present embodiment. In addition, the examples shown below may be appropriately combined as long as they do not cause a contradiction.

[Outline composition of algae culture]
First, the composition when culturing algae will be briefly described. FIG. 1 is a diagram showing an example of a configuration when culturing algae. In FIG. 1, a culture tank 1 is provided. The culture tank 1 is formed in a ring shape in order to improve the flow of the stored liquid, and stores a culture solution in which nutrients and the like suitable for culturing algae are mixed in water. Further, in the culture tank 1, a water wheel 2 is provided on a part of the side surface, and the culture solution is agitated by the water wheel 2 so that the algae in the culture solution do not stay and become uniform. Further, in the culture tank 1, the light 3 is provided in the culture solution. The light 3 is lit in the culture solution. A camera 4 is provided above the light 3. The camera 4 is fixed to the support base 5 so as to photograph the culture solution from above, and performs fixed point observation of the culture solution portion illuminated by the light 3. The camera 4 is arranged so as to capture a predetermined range in which gradation appears in the culture solution by the light from the light 3 arranged in the culture solution at night.

The camera 4 is connected to the dry weight estimation device 10. The camera 4 takes a picture under the control of the dry weight estimation device 10, compresses the shot image into a predetermined format, and outputs the compressed image data to the dry weight estimation device 10. For example, the camera 4 takes a picture under the control of the dry weight estimation device 10, compresses the taken image by lossy compression such as JPEG (Joint Photographic Experts Group), and dries the compressed image data. Output to the weight estimation device 10. The compression format is not limited to lossy compression such as JPEG, and may be lossless compression.

The dry weight estimation device 10 is a device that estimates the dry weight of algae in the culture tank 1 from the data of the compressed image obtained from the camera 4, and is, for example, a computer such as a personal computer or a server computer. The dry weight estimation device 10 may be implemented as one computer, or may be implemented as a cloud with a plurality of computers. In this embodiment, a case where the dry weight estimation device 10 is used as one computer will be described as an example. The dry weight estimation device 10 is a server that is communicably connected to a terminal device used by a user who cultivates algae via a network, receives various operation information from the terminal device, and transmits a processing result to the terminal device. It may be a computer.

[Construction of dry weight estimation device]
Next, the configuration of the dry weight estimation device 10 according to the first embodiment will be described. FIG. 2 is a diagram showing an example of the configuration of the dry weight estimation device.

As shown in FIG. 2, the dry weight estimation device 10 includes an operation unit 20, a display unit 21, an external I / F (interface) unit 22, a storage unit 23, and a control unit 24. The dry weight estimation device 10 may have various functional units of a known computer in addition to the functional units shown in FIG.

The operation unit 20 is an input device that receives inputs for various operations. Examples of the operation unit 20 include an input device that receives input for operations such as a mouse and a keyboard. The operation unit 20 accepts input of various information. The operation unit 20 receives an operation input from the user, and inputs the operation information indicating the received operation content to the control unit 24.

The display unit 21 is a display device that displays various types of information. Examples of the display unit 21 include display devices such as an LCD (Liquid Crystal Display) and a CRT (Cathode Ray Tube). The display unit 21 displays various information. For example, the display unit 21 displays various screens such as an operation screen.

The external I / F unit 22 is an interface for transmitting and receiving various information to and from other devices such as a USB (Universal Serial Bus) port and a communication port. The external I / F unit 22 is connected to the camera 4 and transmits / receives various types of information. For example, the external I / F unit 22 outputs a shooting instruction to the camera 4. Further, the compressed image data output from the camera 4 is input to the external I / F unit 22.

The storage unit 23 is a storage device such as a hard disk, an SSD (Solid State Drive), or an optical disk. The storage unit 23 may be a semiconductor memory in which data such as a RAM (Random Access Memory), a flash memory, and an NVSRAM (Non Volatile Static Random Access Memory) can be rewritten.

The storage unit 23 stores the OS (Operating System) and various programs executed by the control unit 24. For example, the storage unit 23 stores a program that executes a dry weight estimation process described later. Further, the storage unit 23 stores various data used in the program executed by the control unit 24. For example, the control unit 24 stores the compressed image data 30, the prediction model data 31, and the estimation data 32.

The compressed image data 30 is data in which the data of the compressed image input from the camera 4 is stored via the external I / F unit 22.

The prediction model data 31 is data that stores a prediction model used for predicting the dry weight. For example, the prediction model data 31 stores a prediction model that predicts the dry weight from the relationship between the image capacity of the compressed image and the dry weight. The prediction model may be a relational expression showing the relationship between the image capacity of the compressed image and the dry weight, or may be data in a table that stores the relationship between the image capacity of the compressed image and the dry weight.

The estimated data 32 is data that stores the estimated dry weight of the algae.

The control unit 24 is a device that controls the dry weight estimation device 10. As the control unit 24, electronic circuits such as a CPU (Central Processing Unit) and an MPU (Micro Processing Unit), and integrated circuits such as an ASIC (Application Specific Integrated Circuit) and an FPGA (Field Programmable Gate Array) can be adopted. The control unit 24 has an internal memory for storing programs and control data that define various processing procedures, and executes various processing by these. The control unit 24 functions as various processing units by operating various programs. For example, the control unit 24 has an acquisition unit 40, a specific unit 41, a generation unit 42, and an output unit 43. The internal configuration of the control unit 24 is not limited to the configuration shown in FIG. 2, and may be any other configuration as long as it performs the dry weight estimation process described later.

The acquisition unit 40 performs various acquisitions. For example, the acquisition unit 40 controls the camera 4 and acquires a compressed image of the algae culture solution at a predetermined measurement timing. For example, the acquisition unit 40 transmits a shooting instruction to the camera 4 at the measurement timing. As a result, the camera 4 takes a picture, compresses the taken image by lossy compression, and outputs the data of the compressed compressed image to the dry weight estimation device 10.

The acquisition unit 40 acquires the data of the compressed image input from the camera 4, and stores the acquired compressed image data as the compressed image data 30 in the storage unit 23 in association with the shooting date and time. The measurement timing is preferably at night, such as from 8:00 pm to 4:00 am, when the influence of external light such as sunlight is small. The measurement timing is set by the user from, for example, the operation unit 20.

Here, in order to grasp the culture status of algae, it is preferable that the dry weight can be measured regularly and continuously. Therefore, for example, the user sets the measurement timing to midnight every day.

When the set measurement timing is reached, the acquisition unit 40 transmits an imaging instruction to the camera 4, acquires the compressed image data obtained by photographing the culture tank 1, and uses the acquired compressed image data as the compressed image data. It is stored in the storage unit 23 in association with the shooting date and time as 30.

Here, an example of an image of the culture tank 1 will be described. FIG. 3A is a diagram schematically showing an image of a culture tank in a state where the concentration of algae is low. FIG. 3B is a diagram schematically showing an image of a culture tank in a state where the concentration of algae is high. The images of FIGS. 3A and 3B show the light 3 lit in the culture solution, and the changes in the culture solution shown in the photograph are schematically shown by changing the pattern. In the vicinity of the light 3, the culture solution is brightly reflected by the light from the light 3, and in the vicinity of the image far from the light 3, the culture solution is dark. Here, when FIG. 3A and FIG. 3B are compared, there are more regions in FIG. 3A that are brighter due to the light from the light 3. This is because the light of Light 3 is absorbed by the algae in the culture solution, and the lower the concentration of the algae, the farther the light reaches in the culture solution, so that the bright region becomes wider. The dry weight of algae is low when the concentration of algae is low, and high when the concentration of algae is high. As described above, in the image obtained by photographing the culture solution, the lower the dry weight value of the culture solution, the greater the dispersion of the brightness of the entire image. When such an image is compressed, the larger the variance of the luminance, the lower the image compression rate, and therefore the larger the capacity of the image file. That is, the compressed image data 30 has a larger image capacity because the image compression rate is lower as the image obtained by capturing the culture solution having a lower dry weight value.

In addition, the size of the data of the compressed image changes depending on the value of the parameter related to compression. For example, in JPEG, the size of data changes depending on the quality, which is one of the parameters. However, regardless of the value of the parameter related to compression, when the same value is set for shooting, the larger the variance of the brightness, the lower the image compression rate and the larger the capacity of the image file. That is, the compressed image data 30 has a lower image compression rate as the image of the culture solution having a lower dry weight value is photographed when a certain parameter is set and photographed regardless of the set parameter. Therefore, the image capacity becomes large. In this embodiment, no specific setting is made for the parameter of image compression by the camera 4, and the default setting is used.

The dry weight estimation device 10 according to the present embodiment estimates the dry weight of algae contained in the compressed image data 30 by using the relationship between the image capacity and the dry weight.

The identification unit 41 specifies the image capacity of the compressed image data 30 acquired by the acquisition unit 40. For example, the specifying unit 41 specifies the file size of the compressed image data 30 as the image capacity. The specific unit 41 may analyze the compressed image data 30 and specify the data of the portion related to the compressed image as the image capacity, excluding the portion other than the image such as the header and the footer.

The generation unit 42 generates a prediction model for predicting the dry weight from the relationship between the image capacity of the compressed image and the dry weight. For example, the user takes out the culture solution from each of the plurality of compressed image data 30 at the measurement timing at which each image is taken, performs gravimetric analysis of the algae on the culture solution, and dries the culture solution when the compressed image data 30 is photographed. Weigh. The user inputs the dry weight of the measured culture solution and the date when the culture solution was acquired from the operation unit 20. The dry weight of the culture solution may be input from an external measuring device. The generation unit 42 generates a prediction model for a plurality of compressed image data 30 whose dry weight has already been measured, using the image capacity of the compressed image data 30 and the measured dry weight. For example, the generation unit 42 generates a prediction model for predicting the dry weight, assuming that there is a primary correlation represented by the following equation (1) between the image capacity and the dry weight.

Dry weight Y (g / L) = a × image capacity X + b (1)

Here, the parameter a is a parameter representing the slope of the linear function. Parameter b is a parameter representing the intercept of the linear function.

For example, the generation unit 42 performs regression analysis on a plurality of compressed image data 30 whose dry weight has already been measured, using the image capacity of the compressed image data 30 and the measured dry weight, and the parameter a of the formula (1). , Find the parameter b. The generation unit 42 stores the generated prediction model as the prediction model data 31 in the storage unit 23. For example, the generation unit 42 stores the obtained equation (1) in which the obtained parameters a and b are substituted or the parameters a and b as the prediction model data 31 in the storage unit 23. The generation unit 42 may calculate the dry weight for each predetermined image capacity from the equation (1), and store the table showing the relationship between the image capacity and the dry weight in the storage unit 23 as the prediction model data 31. ..

Here, an example of estimation by the prediction model is shown. FIG. 4A is a diagram showing an example of the data used for the regression analysis and the estimation result. In FIG. 4A, "actual measurement", "file", and "estimation" are shown. The measured value indicates the measurement result of the dry weight of the culture solution when the compressed image data 30 was taken. The value of the file B indicates the number of bytes of the image capacity of the compressed image data 30. The estimated value is the estimation result of the dry weight calculated from the image capacity of the compressed image data 30 using the prediction model of the equation (1). In the example of FIG. 4A, nine compressed image data 30 are subjected to regression analysis using the actually measured values and the image capacitance of the compressed image data 30 to generate a prediction model of the equation (1), and the generated prediction. This is the result of estimating the dry weight from the image capacity of the compressed image data 30 using the model. FIG. 4B is a diagram showing a calculation result of regression statistics. FIG. 4B shows the result of calculating the regression statistics for the prediction model shown in FIG. 4A. For example, the multiple correlation R is 0.88079, and it can be seen that there is a strong correlation between the image capacity and the dry weight.

The output unit 43 performs various outputs. For example, the output unit 43 refers to the prediction model data 31 stored in the storage unit 23, and outputs an estimated value of the dry weight of the algae contained in the compressed image based on the image capacity of the compressed image data 30. For example, when the arithmetic expression of the equation (1) is stored in the storage unit 23, the output unit 43 reads the equation (1). When the parameters a and b are stored in the storage unit 23, the output unit 43 sets the parameters a and b in the read-out formula (1). Then, the output unit 43 calculates an estimated value of the dry weight of the algae contained in the compressed image from the image capacity of the compressed image data 30 by using the calculation formula of the formula (1). Then, the output unit 43 outputs the calculated estimated value of the dry weight of the algae to the estimated data 32 in association with the shooting date and time of the compressed image data 30 and stores it. The output unit 43 may output an estimated value of the dry weight of algae to an external device.

The output unit 43 outputs an estimated value of the dry weight of the algae stored in the estimated data 32. For example, the output unit 43 outputs the estimated value of the dry weight stored in the estimation data 32 to the display unit 21 in association with the shooting date in response to the instruction from the operation unit 20. In addition, the output unit 43 calculates an estimated value of the dry weight of the algae contained in the compressed image from the image capacity of each compressed image data 30 stored in the storage unit 23 in response to an instruction from the operation unit 20. You may. Then, the output unit 43 may output the estimated value of the dry weight calculated from each compressed image data 30 to the display unit 21 in association with the shooting date.

Next, an example of a case where the dry weight of algae is estimated by using the dry weight estimation device 10 will be described. In order to grasp the culture status of algae, it is preferable to be able to measure the dry weight regularly and continuously. Therefore, for example, the user sets the measurement timing to midnight every day. When the measurement timing is reached, the dry weight estimation device 10 transmits an imaging instruction to the camera 4, acquires the data of the compressed image obtained by photographing the culture tank 1, and the acquired compressed image data is the compressed image data 30. It is stored in the storage unit 23 in association with the shooting date and time.

The user periodically takes out the culture solution from the culture tank 1 at the measurement timing, performs a general weight analysis of the algae, and measures the dry weight of the algae. The user inputs the dry weight of the measured culture solution and the date when the culture solution was acquired from the operation unit 20.

The dry weight estimation device 10 generates a prediction model using the measured dry weight and the image capacity of the compressed image data 30 when the dry weight is measured. The dry weight estimation device 10 estimates the dry weight from the image capacity of the compressed image data 30 acquired daily by using the generated prediction model.

FIG. 5 is a graph showing an example of the estimation result of the dry weight of algae for a predetermined period. In FIG. 5, the result of estimating the dry weight of algae in December 2016 is shown as "estimation". Further, in FIG. 5, for example, the result of periodically measuring the dry weight of algae is shown as "actual measurement". In the example of FIG. 5, the dry weight of the algae is measured for 2 days continuously for a period of about 1 week, but the present invention is not limited to this, and it is determined according to the conditions such as the environment in which the algae are cultured. Just do it. In the example of FIG. 5, in order to verify the accuracy, a plurality of images are taken at the measurement timing, and the dry weight of the algae is measured from each compressed image data 30. Further, in the example of FIG. 5, the image was not taken on some days.

The dry weight estimation device 10 generates a prediction model using the measured dry weight and the image capacity of the compressed image data 30 when the dry weight is measured. For example, the generation unit 42 determines the measured value of the dry weight of the latest predetermined time (for example, the latest 10 times) or the latest predetermined period (for example, the latest 2 months) and the image capacity of the compressed image data 30. Use to generate a predictive model. A predictive model may be generated each time a new measurement of dry weight is obtained. For example, the generation unit 42 may regenerate the prediction model every time the measurement result of the dry weight is obtained. Further, the prediction model may be generated when the prediction accuracy of the prediction model exceeds a predetermined allowable range. For example, the generation unit 42 may compare the dry weight measurement result with the estimation result each time it is obtained, and regenerate the prediction model when the prediction accuracy exceeds a predetermined allowable range. In addition, the prediction model may be generated periodically. For example, the generation unit 42 may regenerate the prediction model every month or at the timing when the season changes (for example, January, April, July, October). Further, the prediction model may be generated at the timing specified by the user. For example, the generation unit 42 may regenerate the prediction model at the timing when the operation unit 20 receives the instruction to generate the prediction model.

The dry weight estimation device 10 estimates the dry weight from the image capacity of the compressed image data 30 acquired daily by using the generated prediction model. As shown in FIG. 5, the dry weight estimation result is close to the dry weight measurement result, and it can be estimated that the estimation result is sufficiently accurate even on the day when the dry weight is not measured.

In order to grasp the culture status of algae, it is preferable to be able to measure the dry weight on a daily basis, but if the dry weight is measured by general algae weight analysis, the cost of the business will increase. Further, in general algae weight analysis, since the culture solution at a certain point in time is sampled and measured, the current dry weight cannot be known in real time. On the other hand, the dry weight estimation device 10 according to the present embodiment has a simple configuration in which the algae culture solution is imaged by the camera 4, and the daily dry weight of the algae can be measured in real time. In addition, the dry weight estimation device 10 can reduce the number of times the dry weight is measured, and can suppress an increase in the cost of the business. Further, the dry weight estimation device 10 can easily estimate the dry weight of algae by a simple method of specifying the image capacity of the compressed image.

[Processing flow]
Next, the flow of the dry weight estimation process in which the dry weight estimation device 10 estimates the dry weight will be described. FIG. 6 is a flowchart showing an example of the dry weight estimation process. This dry weight estimation process is executed at a predetermined timing, for example, when the compressed image data acquired from the camera 4 is stored in the storage unit 23 as the compressed image data 30.

The identification unit 41 specifies the image capacity of the acquired compressed image data 30 (step S10).

The output unit 43 refers to the prediction model data 31 stored in the storage unit 23, and calculates an estimated value of the dry weight of the algae contained in the compressed image based on the image capacity of the compressed image data 30 (step S11). .. The output unit 43 outputs the calculated estimated dry weight of the algae to the estimated data 32 in association with the shooting date and time of the compressed image data 30 (step S12), and ends the process.

As described above, the dry weight estimation device 10 according to the present embodiment specifies the image capacity of the compressed image when the compressed image obtained by capturing the culture solution of algae is acquired. The dry weight estimation device 10 refers to the storage unit 23 that stores the relationship between the image capacity and the dry weight, and outputs an estimated value of the dry weight of the algae contained in the compressed image based on the specified image capacity. As a result, the dry weight estimation device 10 can easily estimate the dry weight of algae.

Further, the dry weight estimation device 10 according to the present embodiment stores in the storage unit 23 the values of the parameters used in the calculation formula for calculating the dry weight from the image capacity or the calculation formula for calculating the dry weight from the image capacity. .. The dry weight estimation device 10 uses an arithmetic expression stored in the storage unit 23 or an arithmetic expression in which the value of the parameter stored in the storage unit 23 is set, and the algae contained in the compressed image from the specified image capacity. The estimated value of the dry weight of is calculated, and the calculated estimated value is output. As a result, the dry weight estimation device 10 can easily estimate the dry weight of algae.

By the way, although the examples of the present invention have been described so far, the present invention may be implemented in various different forms other than the above-mentioned examples.

For example, in the above embodiment, the case where one image is taken by the camera 4 at the measurement timing and the dry weight of the algae is estimated from the image capacity of the one compressed image taken has been described, but the present invention is limited to this. It's not something. For example, when algae are cultivated outdoors or the like, suspended matter or the like floating in the culture solution may appear in the image, and the suspended matter or the like may become noise. Therefore, it may be configured as follows. For example, the acquisition unit 40 controls the camera 4 to take a plurality of images at the measurement timing, and acquires a plurality of compressed images obtained by capturing the algae culture solution within a predetermined period. The predetermined period may be a period during which the dry weight of the algae in the culture solution of the algae can be regarded as the same. For example, the predetermined period is about 1 minute to several tens of minutes. For example, the acquisition unit 40 controls the camera 4 to take a plurality of images at predetermined intervals (for example, 10-second intervals) in 1 minute at the measurement timing. The identification unit 41 specifies the image capacity of the data of the plurality of compressed images acquired by the acquisition unit 40, respectively. The output unit 43 obtains an estimated dry weight from the image capacitance of each of the plurality of compressed images with reference to the prediction model data 31 stored in the storage unit 23, and obtains an average value of the obtained estimated dry weights. calculate. Then, the output unit 43 may output and store the average value of the estimated dry weight values in the estimated data 32 in association with the date and time of the measurement timing at which the imaging was performed. In the culture tank 1, the culture solution is agitated by the water wheel 2, and suspended matter is flowing. Therefore, by calculating the dry weight from the image capacity of the data of a plurality of captured compressed images, it is possible to estimate the dry weight of algae while suppressing the influence of noise due to suspended matter or the like. The dry weight estimation device 10 excludes the captured image as an image showing floating substances and the like, acquires a compressed image without floating substances and the like, and calculates the dry weight of algae from the image capacity of the acquired compressed image. You may estimate. For example, the dry weight estimation device 10 compares the captured images with each other to obtain the amount of change from other images, and excludes an image in which the amount of change is equal to or greater than a predetermined allowable amount as an image showing a floating substance or the like. May be good. Further, the dry weight estimation device 10 may detect an object such as a suspended substance in the region of the culture solution from the captured image, and may remove the image in which the object is detected as an image showing the suspended substance or the like. ..

Further, in the above embodiment, the case where the image captured by the camera 4 is compressed into a predetermined format and the data of the compressed compressed image is output is described, but the present invention is not limited to this. For example, the camera 4 may output the data of the captured image as it is, and the dry weight estimation device 10 may compress the image into a predetermined format.

Further, in the above embodiment, the case where the first-order arithmetic expression of the equation (1) is used as the prediction model has been described, but the present invention is not limited to this. For example, if the dry weight can be predicted from the image capacity, any existing model calculation formula may be used as the prediction model.

Further, in the above-mentioned examples, the dry weight of algae is estimated as an example in the examples, but the present invention is not limited to this. Since the dry weight estimation device 10 can be applied to, for example, the estimation of the concentration of the aqueous solution accompanied by the change in the color-matching component of brightness, the estimation of the biomass of other organisms can also be applied.

In addition, among the processes described in this embodiment, all or part of the processes described as being automatically performed can also be performed manually. Alternatively, all or part of the processing described as being performed manually can be automatically performed by a known method. In addition, the processing procedure, control procedure, specific name, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified.

Further, each component of each of the illustrated devices is a functional concept, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure. That is, all or a part thereof can be functionally or physically distributed / integrated in an arbitrary unit according to various loads, usage conditions, and the like. For example, the function of the dry weight estimation device 10 may be distributed and implemented in a plurality of servers. Further, each processing function performed by each device may be realized by a CPU and a program analyzed and executed by the CPU, or may be realized as hardware by wired logic.

[system]
Although each embodiment of the disclosed system has been described so far, an example of the hardware configuration of the dry weight estimation device 10 in each embodiment will be described. The various processing functions performed by each device may be executed on the CPU (or a microcomputer such as an MPU or MCU (Micro Controller Unit)) in whole or in any part thereof. Further, various processing functions may be executed in whole or in any part on a program analyzed and executed by a CPU (or a microcomputer such as an MPU or MCU) or on hardware by wired logic. Needless to say, it's good. The various processes described in each of the above embodiments can be realized by executing a program prepared in advance on a computer. Therefore, in the following, as an example of the hardware configuration, an example of a computer that executes a program having the same functions as those in each of the above embodiments will be described.

FIG. 7 is a diagram showing an example of a hardware configuration. The dry weight estimation device 10 can be realized by the same hardware configuration as the computer 7000 shown in FIG. As shown in FIG. 7, the computer 7000 includes a processor 7001 that executes various arithmetic processes, an input / output device 7002, and a communication device 7003. Further, the computer 7000 has a RAM 7004 for temporarily storing various information and a hard disk device 7005. Further, each of the devices 7001 to 7005 is connected to the bus 7006.

The hard disk device 7005 stores a component position detection program having the same functions as the processing units of the acquisition unit 40, the specific unit 41, the generation unit 42, and the output unit 43 shown in each of the above embodiments. Various data for realizing the component position detection program are stored in the hard disk device 7005. For example, the hard disk device 7005 stores compressed image data 30, prediction model data 31, and estimation data 32.

The processor 7001 reads each program stored in the hard disk device 7005, expands it into the RAM 7004, and executes it to perform various processes. Further, these programs can make the computer 7000 function as the acquisition unit 40, the specific unit 41, the generation unit 42, and the output unit 43 shown in each of the above embodiments. It should be noted that each of the above programs does not necessarily have to be stored in the hard disk device 7005. For example, the computer 7000 may read and execute the program stored in the storage medium that can be read by the computer 7000.

The following additional notes will be further disclosed with respect to the embodiments including the above embodiments.

(Appendix 1) When a compressed image of an algae culture solution is acquired, the image capacity of the compressed image is specified.
With reference to a storage unit that stores the relationship between the image capacity and the dry weight, an estimated value of the dry weight of the algae contained in the compressed image is output based on the specified image capacity.
A dry weight estimation program characterized by having a computer perform the process.

(Appendix 2) The relationship stored in the storage unit is a parameter used in a calculation formula for calculating the dry weight from the image capacity or a calculation formula for calculating the dry weight from the image capacity.
The output process uses an arithmetic expression stored in the storage unit or an arithmetic expression in which the value of the parameter stored in the storage unit is set, and the algae contained in the compressed image from the specified image capacity. Including the process of calculating the estimated value of the dry weight of
The dry weight estimation program according to Appendix 1, wherein the dry weight estimation program is described.

(Appendix 3) In the specifying process, when a plurality of compressed images obtained by capturing a plurality of compressed images of algae culture solution within a predetermined period are acquired, the image capacities of the plurality of compressed images are specified.
In the output process, the estimated value of the dry weight is obtained from the image capacitance of each of the plurality of compressed images with reference to the storage unit, and the average value of the obtained estimated dry weight is obtained as the output estimated value. To output,
The dry weight estimation program according to Appendix 1 or 2, wherein the dry weight estimation program is described.

(Appendix 4) When a compressed image of the algae culture solution is acquired, the image capacity of the compressed image is specified.
With reference to a storage unit that stores the relationship between the image capacity and the dry weight, an estimated value of the dry weight of the algae contained in the compressed image is output based on the specified image capacity.
A dry weight estimation method characterized in that the processing is performed by a computer.

(Appendix 5) An acquisition unit that photographs a culture solution in which algae are cultured and acquires the compressed image from an imaging unit that generates a compressed image of the captured image.
A specific unit that specifies the image capacity of the compressed image acquired by the acquisition unit, and
An output unit that outputs an estimated value of the dry weight of algae contained in the compressed image based on the specified image capacity by referring to a storage unit that stores the relationship between the image capacity and the dry weight.
A dry weight estimator with.

(Appendix 6) The photographing unit captures the image including a predetermined range in which gradation appears in the culture solution by light from a light source arranged in the culture solution.
The dry weight estimation device according to Appendix 5, wherein the dry weight is estimated.

(Appendix 7) The dry weight estimation device according to Appendix 5 or 6, wherein the photographing unit photographs a culture solution in which algae are cultured at a predetermined timing at night.

(Appendix 8) The photographing unit is any one of Appendix 5 to 7, characterized in that the imaging unit photographs a culture solution in which algae are cultured and generates a compressed image of the photographed image, with a constant compression parameter. The dry weight estimator according to.

1 Culture tank 2 Water wheel 3 Light 4 Camera 10 Dry weight estimation device 20 Operation unit 21 Display unit 22 External I / F unit 23 Storage unit 24 Control unit 30 Compressed image data 31 Prediction model data 32 Estimated data 40 Acquisition unit 41 Specific unit 42 Generation unit 43 Output unit

Claims (6)

When a compressed image of the algae culture solution is acquired, the image capacity of the compressed image is specified.
With reference to a storage unit that stores the relationship between the image capacity and the dry weight, an estimated value of the dry weight of the algae contained in the compressed image is output based on the specified image capacity.
A dry weight estimation program characterized by having a computer perform the process. The relationship stored in the storage unit is a parameter used in the calculation formula for calculating the dry weight from the image capacity or the calculation formula for calculating the dry weight from the image capacity.
The output process uses an arithmetic expression stored in the storage unit or an arithmetic expression in which the value of the parameter stored in the storage unit is set, and the algae contained in the compressed image from the specified image capacity. Including the process of calculating the estimated value of the dry weight of
The dry weight estimation program according to claim 1. In the specifying process, when a plurality of compressed images obtained by capturing a plurality of compressed images of algae culture solution within a predetermined period are acquired, the image capacities of the plurality of compressed images are specified.
In the output process, the estimated value of the dry weight is obtained from the image capacitance of each of the plurality of compressed images with reference to the storage unit, and the average value of the obtained estimated dry weight is obtained as the output estimated value. To output,
The dry weight estimation program according to claim 1 or 2. When a compressed image of the algae culture solution is acquired, the image capacity of the compressed image is specified.
With reference to a storage unit that stores the relationship between the image capacity and the dry weight, an estimated value of the dry weight of the algae contained in the compressed image is output based on the specified image capacity.
A dry weight estimation method characterized in that the processing is performed by a computer. An acquisition unit that photographs the culture solution in which algae are cultured and acquires the compressed image from an imaging unit that generates a compressed image of the captured image.
A specific unit that specifies the image capacity of the compressed image acquired by the acquisition unit, and
An output unit that outputs an estimated value of the dry weight of algae contained in the compressed image based on the specified image capacity by referring to a storage unit that stores the relationship between the image capacity and the dry weight.
A dry weight estimator with. The photographing unit captures the image including a predetermined range in which gradation appears in the culture solution by light from a light source arranged in the culture solution.
The dry weight estimation device according to claim 5.

Images