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WO2018179220A1 - Système informatique, procédé d'établissement de diagnostic sur plante et programme - Google Patents

Système informatique, procédé d'établissement de diagnostic sur plante et programme Download PDF

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Publication number
WO2018179220A1
WO2018179220A1 PCT/JP2017/013253 JP2017013253W WO2018179220A1 WO 2018179220 A1 WO2018179220 A1 WO 2018179220A1 JP 2017013253 W JP2017013253 W JP 2017013253W WO 2018179220 A1 WO2018179220 A1 WO 2018179220A1
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WIPO (PCT)
Prior art keywords
plant
image
analysis
result
image information
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PCT/JP2017/013253
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English (en)
Japanese (ja)
Inventor
俊二 菅谷
佳雄 奥村
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株式会社オプティム
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Priority to JP2018522167A priority Critical patent/JPWO2018179220A1/ja
Priority to PCT/JP2017/013253 priority patent/WO2018179220A1/fr
Publication of WO2018179220A1 publication Critical patent/WO2018179220A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

Definitions

  • the present invention relates to a computer system, a plant diagnosis method, and a program for diagnosing a plant.
  • Non-Patent Document 1 a disease or quality is determined only by acquired image data or environmental data, and a plurality of data resources are not analyzed and predicted.
  • the present invention analyzes a correlation between possible prediction results (for example, illness and quality) from image analysis and a plurality of data resources, and performs computer system, plant that performs prediction with higher accuracy than single image analysis
  • An object is to provide a diagnostic method and a program.
  • the present invention provides the following solutions.
  • the present invention is a computer system for diagnosing a plant, Plant image acquisition means for acquiring a plant image of the plant; Plant image analysis means for image analysis of the acquired plant image; Outside-image information acquisition means for acquiring outside-image information of the plant, A non-image information analyzing means for analyzing the acquired non-image information; Correlation analysis means for analyzing the correlation between the image analysis result and the analysis result; A plant diagnostic means for diagnosing the plant based on the analyzed correlation result; A computer system is provided.
  • a computer system for diagnosing a plant acquires a plant image of the plant, performs image analysis on the acquired plant image, acquires non-image information of the plant, and acquires the acquired non-image information. Analyzing and analyzing the correlation between the image analysis result and the analyzed result, and diagnosing the plant based on the analyzed correlation result.
  • the present invention is a computer system category, but the same functions and effects according to the category are exhibited also in other categories such as a plant diagnosis method and a program.
  • a computer system for analyzing a correlation between possible prediction results from image analysis and a plurality of data resources and performing prediction with higher accuracy than analysis of a single image. It becomes possible to provide.
  • FIG. 1 is a diagram showing an outline of a plant diagnosis system 1.
  • FIG. 2 is an overall configuration diagram of the plant diagnosis system 1.
  • FIG. 3 is a functional block diagram of the computer 10.
  • FIG. 4 is a flowchart showing the image analysis learning process executed by the computer 10.
  • FIG. 5 is a flowchart showing a learning process for analyzing out-of-image information executed by the computer 10.
  • FIG. 6 is a flowchart showing a plant diagnosis process executed by the computer 10. It is.
  • FIG. 1 is a diagram for explaining an outline of a plant diagnosis system 1 which is a preferred embodiment of the present invention.
  • the plant diagnosis system 1 is a computer system that includes a computer 10 and diagnoses a plant.
  • the computer 10 includes various imaging devices such as a visible light camera, an infrared camera, an ultraviolet camera, an X-ray camera, a CT (Computed Tomography), and an ultrasonic camera (not shown), temperature (water temperature, temperature, etc.) data, and humidity data. It is a computing device that is communicably connected to various sensor devices that measure sunlight illuminance data, water flow data, salinity concentration data, and the like.
  • the computer 10 acquires a plant image of a plant (step S01).
  • the computer 10 acquires at least one of a visible light image, an infrared image, an ultraviolet image, an X-ray image, a CT scan image, or an ultrasonic image as a plant image.
  • a visible light image of nori is acquired.
  • the computer 10 performs image analysis on the acquired plant image (step S02).
  • the computer 10 analyzes the feature amount (luminance, color, particle, shape, etc.) of the plant image.
  • the computer 10 analyzes, for example, the presence or absence of spots, the presence or absence of discoloration, the presence or absence of color loss, the presence or absence of discoloration, etc. from a visible light image of seaweed, and causes redness, fungal disease, bud disease, or whitening. Analyzes about diseases, etc.
  • the computer 10 learns by associating a plant image stored in advance with a diagnosis result performed on the plant image, and analyzes the plant image acquired this time based on the learned result. Good.
  • the computer 10 acquires information outside the image of the plant (step S03).
  • the computer 10 acquires at least one of temperature data, humidity data, sunlight illuminance data, water flow data, or salinity concentration data acquired by various sensor devices as information outside the image. In the following description, it demonstrates as what acquired sunlight illumination intensity data.
  • the computer 10 analyzes the acquired out-of-image information (step S04). For example, the computer 10 analyzes whether temperature data, humidity data, sunlight illuminance data, water flow data, salinity concentration data, and the like are appropriate for growth. The computer 10 learns by associating the pre-stored non-image information with the diagnosis result performed on the non-image information, and analyzes the non-image information acquired this time based on the learned result. Also good.
  • the computer 10 analyzes the correlation between the image analysis result and the analysis result (step S05). For example, when the computer 10 obtains a result that there is a red spot as a result of image analysis, and obtains a result that the sunlight illuminance data is not appropriate as an analysis result, the computer 10 analyzes these correlations.
  • the computer 10 diagnoses a plant based on the analyzed correlation result (step S06). For example, the computer 10 determines that the disease name of the plant is reddish based on the analyzed correlation result.
  • FIG. 2 is a diagram showing a system configuration of the plant diagnosis system 1 which is a preferred embodiment of the present invention.
  • the plant diagnosis system 1 is a computer system that includes a computer 10 and a public line network (Internet network, third generation, fourth generation communication network, etc.) 5 and diagnoses plants.
  • the plant diagnosis system 1 is a computer system that diagnoses seaweed.
  • the computer 10 is the above-described computing device having the functions described later.
  • FIG. 3 is a functional block diagram of the computer 10.
  • the computer 10 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), etc. as the control unit 11, and a device for enabling communication with other devices as the communication unit 12. For example, a WiFi (Wireless Fidelity) compatible device compliant with IEEE 802.11 is provided.
  • the computer 10 also includes a data storage unit such as a hard disk, a semiconductor memory, a recording medium, or a memory card as the storage unit 13. Further, the computer 10 includes, as the processing unit 14, a device for executing various processes such as image processing, state diagnosis, and learning process.
  • control unit 11 reads a predetermined program, thereby realizing a plant image acquisition module 20, a diagnosis result acquisition module 21, and an outside-image information acquisition module 22 in cooperation with the communication unit 12. Further, in the computer 10, the control unit 11 reads a predetermined program, thereby realizing the storage module 30 in cooperation with the storage unit 13. Further, in the computer 10, the control unit 11 reads a predetermined program, so that the learning module 40, the image analysis module 41, the extra-image information analysis module 42, the correlation analysis module 43, the diagnosis are cooperated with the processing unit 14. A module 44 is realized.
  • FIG. 4 is a flowchart of the image analysis learning process executed by the computer 10. Processing executed by each module described above will be described together with this processing.
  • Plant image acquisition module 20 acquires a plant image of a plant (step S10).
  • the plant image acquired by the plant image acquisition module 20 is, for example, at least one of a visible light image, an infrared image, an ultraviolet image, an X-ray image, a CT scan image, or an ultrasonic image.
  • the plant image acquisition module 20 may acquire these plant images from a corresponding imaging device, or may acquire them via a computer or the like (not shown). In the following description, the plant image acquisition module 20 is described as acquiring a visible light image of laver as a plant image.
  • the plant image acquisition module 20 may acquire a plant image from a database or the like stored in an external computer (not shown).
  • the storage module 30 stores plant images (step S11).
  • step S11 the storage module 30 identifies the identifier of the plant that acquired the plant image this time (plant name, management number, preset reference number, identifier that can uniquely identify other plants, etc.), the plant image, Are stored in association with each other. Note that the storage module 30 may store only plant images.
  • the diagnosis result acquisition module 21 acquires the diagnosis result of the plant corresponding to the plant image acquired this time (step S12).
  • the diagnosis result acquisition module 21 acquires a diagnosis result from, for example, an external computer (not shown) that stores a database regarding plant diseases, a terminal device owned by the producer, or the like.
  • the diagnosis result in the present embodiment is, for example, identification of disease name, identification of symptoms, identification of necessary treatment, and the like.
  • the learning module 40 learns by associating the plant image stored in the storage module 30 with the diagnosis result acquired by the diagnosis result acquisition module 21 (step S13). In step S13, the learning module 40 learns at least one of the above-described visible light image, infrared image, ultraviolet image, X-ray image, CT scan image, or ultrasonic image in association with the diagnosis result.
  • the storage module 30 stores the learned result (step S14).
  • the plant diagnosis system 1 executes the above-described image analysis learning process a sufficient number of times and stores the learning result.
  • FIG. 5 is a diagram illustrating a flowchart of the extra-image information analysis learning process executed by the computer 10. Processing executed by each module described above will be described together with this processing.
  • the non-image information acquisition module 22 acquires plant non-image information (step S20).
  • the non-image information acquired by the non-image information acquisition module 22 is, for example, at least one of temperature data, humidity data, sunlight illuminance data, water flow data, or salinity concentration data measured by various sensors.
  • the non-image information acquisition module 22 may acquire these non-image information from various corresponding devices or the like, or may acquire via a computer or the like not shown. In the following description, the non-image information acquisition module 22 will be described as acquiring sunlight illuminance data as the non-image information.
  • the storage module 30 stores information outside the image (step S21).
  • the storage module 30 associates the identifier of the plant that acquired the information outside the image this time (the name of the plant, the management number, a preset reference number, an identifier that can uniquely identify other plants, etc.) and the information outside the image. Add and remember. Note that the storage module 30 may store only non-image information.
  • the diagnosis result acquisition module 21 acquires the diagnosis result of the plant corresponding to the information outside the image acquired this time (step S22).
  • the diagnosis result acquisition module 21 acquires a diagnosis result from, for example, an external computer (not shown) that stores a database regarding plant diseases, a terminal device owned by the producer, or the like.
  • the diagnosis result in the present embodiment is, for example, identification of disease name, identification of symptoms, identification of necessary treatment, and the like.
  • the diagnostic result acquisition module 21 may acquire a diagnostic result from a diagnostic result stored in an external computer (not shown).
  • the learning module 40 learns by associating the non-image information stored in the storage module 30 with the diagnosis result acquired by the diagnosis result acquisition module 21 (step S23). In step S23, the learning module 40 learns at least one of the above-described temperature data, humidity data, sunlight illuminance data, water flow data, or salinity concentration data in association with the diagnosis result.
  • the storage module 30 stores the learned result (step S24).
  • the plant diagnosis system 1 executes the above-described image information analysis learning process a sufficient number of times and stores the learning result.
  • the above is the learning process for analyzing information outside the image.
  • FIG. 6 is a diagram illustrating a flowchart of the plant diagnosis process executed by the computer 10. Processing executed by each module described above will be described together with this processing. In the following description, the plant diagnosis system 1 will be described as diagnosing a plant based on a visible light image and sunlight illuminance data.
  • Plant image acquisition module 20 acquires a plant image of a plant (step S30).
  • the plant image acquired by the plant image acquisition module 20 acquires, for example, at least one of a visible light image, an infrared image, an ultraviolet image, an X-ray image, a CT scan image, or an ultrasonic image.
  • the plant image acquisition module 20 may acquire these plant images from a corresponding imaging device, or may acquire them via a computer or the like (not shown).
  • the image analysis module 41 performs image analysis on the acquired plant image (step S31).
  • step S31 a plant image is image-analyzed based on the result learned by the learning module 40.
  • the image analysis module 41 analyzes the feature amount (luminance, color, particle, shape, etc.) of the plant image acquired this time.
  • the image analysis module 41 analyzes a plurality of candidates such as image parts and features necessary for making a diagnosis from the learning result.
  • the image analysis module 41 analyzes, for example, RGB values of plant images.
  • the image analysis module 41 analyzes the shape by executing edge extraction or the like.
  • the image analysis module 41 analyzes the presence or absence of spots, the presence or absence of discoloration, the presence or absence of color loss, the presence or absence of color fading from a visible light image of seaweed, and causes redness, blight fungus disease, bud disease, Analyzes for white spots and other diseases.
  • the image analysis module 41 is not necessarily limited to identifying a disease name or symptom as a result of image analysis, and may only obtain information for diagnosis described later. For example, the image analysis module 41 may obtain preliminary information for diagnosis from the result of image analysis.
  • the non-image information acquisition module 22 acquires plant non-image information (step S32).
  • the non-image information acquisition module 22 acquires at least one of temperature data, humidity data, sunlight illuminance data, water flow data, or salinity concentration data measured by various sensor devices as the non-image information.
  • the non-image information acquisition module 22 may acquire these non-image information from various corresponding devices or the like, or may acquire via a computer or the like not shown.
  • the non-image information analysis module 42 analyzes the acquired non-image information (step S33).
  • step S33 the non-image information analysis module 42 analyzes the non-image information based on the result learned by the learning module 40.
  • the extra-image information analysis module 42 analyzes a plurality of candidates for extra-image information necessary for making a diagnosis from the learned result.
  • the non-image information analysis module 42 analyzes, for example, whether or not the non-image information is appropriate for plant growth.
  • the non-image information analysis module 42 acquires, for example, sunlight illuminance data as the non-image information, and analyzes that there is a possibility that some disease has occurred when the sunlight illuminance data is not appropriate.
  • the non-image information analysis module 42 is not necessarily limited to identifying a disease name or symptom as a result of analysis, and may only obtain information for diagnosis described later. For example, the extra-image information analysis module 42 may obtain preliminary information for diagnosis from the analysis result.
  • the correlation analysis module 43 analyzes the correlation between the result of the image analysis by the image analysis module 41 and the result of the analysis by the extra-image information analysis module 42 (step S34).
  • step S34 for example, the correlation analysis module 43 calculates the sunlight illuminance data as a result of analysis by the image analysis module 41 as an analysis result of red spots and as a result of analysis by the non-image information analysis module 42. Is not appropriate, and the correlation with the analysis result that there is a suspicion that some kind of disease has occurred is analyzed.
  • the correlation analysis module 43 analyzes the correlation as a score. That is, the correlation analysis module 43 evaluates as a score how much the diagnosis obtained from the analysis result and the diagnosis obtained from the analysis result have a correlation.
  • the correlation analysis module 43 evaluates the degree of correlation between the diagnosis results of the respective items obtained as a result of the analysis and the diagnosis results of the respective items obtained as a result of the analysis.
  • the correlation analysis module 43 evaluates a high correlation as a high score, and evaluates a low correlation as a low score.
  • the diagnosis module 44 diagnoses a plant based on the analyzed correlation result (step S35).
  • step S ⁇ b> 35 the diagnosis module 44 identifies the disease name of the plant based on the combination of the evaluation results having the highest score as the result of the analyzed correlation. For example, the diagnosis module 44 determines that the current plant has a redness disease based on the above-described image analysis result, the above-described analysis result of the non-image information, and the correlation analysis result. Is identified.
  • the learning module 40 learns the current diagnosis result in association with the plant image and the information outside the image (step S36).
  • the storage module 30 stores the learned result (step S37).
  • the computer 10 performs a diagnosis in consideration of the result learned this time at the subsequent diagnosis.
  • the computer 10 may transmit the diagnosis result to a terminal device or the like owned by a producer (not shown).
  • the diagnosis result may be transmitted to this terminal device.
  • various information such as a specific treatment method and a risk level may be transmitted together with the diagnosis result.
  • the terminal device that has received the various types of information may notify the various types of information by display or voice.
  • the above is the plant diagnosis process.
  • the means and functions described above are realized by a computer (including a CPU, an information processing apparatus, and various terminals) reading and executing a predetermined program.
  • the program is provided, for example, in a form (SaaS: Software as a Service) provided from a computer via a network.
  • the program is provided in a form recorded on a computer-readable recording medium such as a flexible disk, CD (CD-ROM, etc.), DVD (DVD-ROM, DVD-RAM, etc.).
  • the computer reads the program from the recording medium, transfers it to the internal storage device or the external storage device, stores it, and executes it.
  • the program may be recorded in advance in a storage device (recording medium) such as a magnetic disk, an optical disk, or a magneto-optical disk, and provided from the storage device to a computer via a communication line.

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Abstract

Le problème décrit par la présente invention est de fournir un système informatique, un procédé d'établissement de diagnostic sur une plante et un programme qui analysent, à partir d'une analyse d'image et d'une pluralité de ressources de données, la corrélation entre des résultats de prédiction potentiels et qui réalisent des prédictions ayant un degré d'exactitude plus élevé que l'analyse simple d'images. La solution selon l'invention porte sur un système informatique destiné à établir un diagnostic sur une plante, qui acquiert une image de plante de la plante, analyse l'image de plante acquise, acquiert des informations de non-image concernant la plante, analyse les informations de non-image acquises, analyse la corrélation entre les résultats de l'analyse de l'image et les résultats de l'analyse des informations, et établit un diagnostic sur la plante sur la base des résultats de la corrélation analysée.
PCT/JP2017/013253 2017-03-30 2017-03-30 Système informatique, procédé d'établissement de diagnostic sur plante et programme WO2018179220A1 (fr)

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JP2018522167A JPWO2018179220A1 (ja) 2017-03-30 2017-03-30 コンピュータシステム、植物診断方法及びプログラム
PCT/JP2017/013253 WO2018179220A1 (fr) 2017-03-30 2017-03-30 Système informatique, procédé d'établissement de diagnostic sur plante et programme

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