JP2020005572A - Phytopathogenic fungus detection device as well as detection method and pesticide selection method of using the same - Google Patents

Abstract

To provide phytopathogenic fungus detection device that can present an effective pesticide for the phytopathogenic fungus before onset of a fungal disease while selectively determining whether a test sample contains a phytopathogenic fungus, and to provide detection methods of phytopathogenic fungus and selection methods of effective pesticide.SOLUTION: Provided is a phytopathogenic fungus detecting device having a plurality of sensor-like devices having an artificial cell wall, a test sample input unit provided on the upper part of the artificial cell wall, and a culture solution storage unit provided at the lower part of the artificial cell wall, and an optical observation unit that detects hyphal growth in each sensor-like device, characterized by containing respectively a different pesticidal bulk in the culture solution storage unit.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an apparatus for detecting a phytopathogenic fungus, a method for detecting a pathogenic fungus using the same, and a method for selecting an agricultural chemical.

  For phytopathogenic fungi, as a property related to plant invasion, after forming and attaching an appressorium on the plant surface, pores such as stoma tissue are searched and hyphae are extended into the plant from there. It has features such as secretion of decomposing enzymes (cellulase, pectinase).

  Utilizing these, for example, Patent Document 1 discloses a fungal weighing method using a microporous membrane support. Further, in Non-Patent Document 1, pseudohyphae of Phytophthora sojae, which is a kind of phytopathogenic oomycetes, try to dive below the horizontal growth, and PET (polyethylene terephthalate) having a pore of 3 μm. It discloses piercing the membrane.

  In addition, paying attention to this property, the present inventors have already proposed a method for determining phytopathogenic oomycetes (Patent Document 2).

JP 2005-287337 A Patent No. 6167309

Paul F. Morris. et. al. "Chemotropic and Contact Responses of Phytophthora sojae Hyphae to Soybean Isoflavonoids and Artificial Substrates", Plant Physiol. (1998) 117: 1171-1178.

  The technology described in the background art enables selective detection of phytopathogenic fungi.However, at the plant cultivation site, it is necessary to respond after detecting the pathogenic fungi, that is, to suppress or eliminate the growth of pathogenic fungi by pesticide administration. It is said. In this regard, in the prior art known so far, after the onset by a pathogenic fungus, the pathogen is presumed from the symptoms and administration is performed, or the administration is performed after separating and identifying the pathogen, and the spread cannot be prevented The situation happens. Alternatively, measures such as excessive pre-medication of pesticides may be taken on the premise of the onset, which has led to heavy labor in farm management work. This overdose is thought to further lead to the emergence of resistant bacteria, and even with efforts, the disease cannot be suppressed, and in some cases, crops are reduced and crops are abandoned. is there.

  The present invention has been made in view of such circumstances, and its purpose is to selectively determine whether a test sample contains a phytopathogenic fungus and to determine an effective pesticide for the phytopathogenic fungus. It is an object of the present invention to provide a detection device, a detection method, and a method for selecting an effective pesticide, which can be presented before the onset of a fungal disease.

  As a result of intensive studies, the present inventors have found that the above-described problems can be solved by a detection device having the following configuration, and have completed further studies based on such findings.

  That is, a detection device according to one aspect of the present invention is a sensor-like device having an artificial cell wall, a test sample input section provided at an upper portion of the artificial cell wall, and a culture solution storage section provided at a lower portion of the artificial cell wall. It is characterized by having a plurality of devices and an optical observation unit for detecting hyphal growth in each sensor-like device, wherein the culture solution storage unit contains different agrochemical ingredients.

  ADVANTAGE OF THE INVENTION According to this invention, the apparatus and method which can detect a phytopathogenic fungus easily and safely can be provided. Further, according to the present invention, it is possible to select an effective pesticide before the onset of a fungal disease, and it becomes possible to suppress the labor for spraying useless pesticides and excessive medication, etc., which is very useful for industrial use. is there.

FIG. 1 is a schematic sectional view showing a part of a detection device according to an embodiment of the present invention. FIG. 2 is a schematic cross-sectional view illustrating an example of the artificial cell wall provided in the sensor-like device in the detection device of the present embodiment. FIG. 3 is a top view illustrating an example of the detection device according to the present embodiment. FIG. 4 is a perspective view illustrating an example of the detection device according to the present embodiment. FIG. 5 is a top view illustrating an example of the arrangement of the sensor-like devices in the detection device according to the present embodiment. FIG. 6 is a top view illustrating another example of the arrangement of the sensor-like devices in the detection device according to the present embodiment. FIG. 7 is a schematic diagram illustrating a detection operation of the detection device of the present embodiment. FIG. 8 is a schematic diagram illustrating an example of a case where an agricultural chemical is selected using the detection device of the present embodiment. FIG. 9 is a top view illustrating an example of a detection device according to a further aspect of the present invention. FIG. 10 is a top view showing an example of a detection device according to still another aspect of the present invention. FIG. 11 is a schematic diagram showing each region in the detection device shown in FIG. FIG. 12 is a diagram illustrating an example of a network configuration of an information processing system to which the sensor according to the embodiment of the present disclosure is applied. FIG. 13 is a block diagram showing an example of a detailed configuration of the information processing system shown in FIG. FIG. 14 is a diagram illustrating an example of a data configuration of various tables stored in the memory of the server. FIG. 15 is a diagram illustrating an example of a configuration of a measurement data table stored in the memory of the sensor. FIG. 16 is a diagram illustrating an example of the data configuration of various tables stored in the memory of the user terminal. FIG. 17 is a diagram illustrating an example of a data configuration of various tables stored in the advisor terminal. FIG. 18 is a sequence diagram illustrating an example of processing of the information processing system illustrated in FIG. FIG. 19 is a flowchart illustrating an example of processing of the server in the normal phase. FIG. 20 is a diagram illustrating an example of a notification screen displayed by the advisor terminal that has received the information regarding the detection of pathogenic bacteria. FIG. 21A is a diagram illustrating an example of an advice screen displayed on the user terminal that has received the advice information. FIG. 21B is a diagram illustrating an example of an advice screen displayed on the user terminal that has received the advice information. FIG. 22 is a graph showing the results of Example 1. FIG. 23 is a graph showing the results of Example 2. FIG. 24 is a graph showing the results of Example 3.

  Hereinafter, embodiments according to the present invention will be specifically described, but the present invention is not limited thereto.

(Detection device)
As shown in FIG. 1, the apparatus for detecting a phytopathogenic fungus according to the present embodiment includes an artificial cell wall 2, a test sample input unit 3 provided above the artificial cell wall 2, and an artificial cell wall 2. It has a plurality of sensor-like devices 1 having a culture solution storage section 4 provided at the lower part. Further, the apparatus of the present embodiment has an optical observation unit 7 for detecting hyphal growth in each sensor-like device. Further, the culture solution storage unit 4 contains different pesticide active ingredients 6.

  The test sample input unit 3 is a container for inputting the test sample 5, and the bottom surface of the test sample input unit 3 is formed by the artificial cell wall 2.

  As shown in FIG. 2, the artificial cell wall 2 preferably includes at least a substrate 21 having a through hole 22 and a cellulose membrane 23 provided on one surface of the substrate 21. The back surface of the substrate 21 is in contact with the front surface 23a of the cellulose film 23. By using such an artificial cell wall, it becomes easier to selectively detect the target phytopathogenic fungus.

The through hole 22 penetrates from the front surface to the back surface of the substrate 21, and the hole diameter of the through hole is preferably ^{2 to} 7 μm (cross sectional area of 4.5 to 38.5 μm ² ). When the pore size is within the above range, the target pathogenic fungus can be more reliably and selectively detected.

  In order to more reliably and selectively detect the target pathogenic fungus, it is preferable to adjust the thickness of the cellulose membrane 23. Specifically, the thickness of the cellulose film 23 is preferably 0.5 to 2 μm.

  In the artificial cell wall 2 of the present embodiment, by adjusting the pore diameter of the through-holes 22 of the substrate 21 and the thickness of the cellulose membrane 23 within the above ranges, the non-phytopathogenic fungi that do not penetrate the cellulose membrane 23 may be reduced. In contrast, the pathogenic fungus targeted in the present embodiment selectively appears on the back surface of the cellulose membrane 23, and thus it is considered that the non-plant pathogenic fungus can be selectively detected.

  The thickness of the substrate 21 is not particularly limited, but is preferably about 5 to 150 μm as an example.

  As shown in FIG. 1, a test sample 5 is supplied into the test sample input unit 3. Thus, when the test sample 5 contains a phytopathogenic fungus, the phytopathogenic fungus exists on the front surface of the substrate 21.

  In the present embodiment, the test sample 5 is a solid, a liquid, or a gas. The test sample 5 is desirably a solid or a liquid. Examples of solid test samples 5 are soil or crushed plants. Other examples are agricultural materials such as vermiculite, rock wool, or urethane. Examples of liquid test sample 5 are used to wash agricultural water, solutions used for hydroponics, liquids used to wash plants, liquids extracted from plants, agricultural materials. Liquid after cleaning, or liquid used for washing clothes or shoes of an operator.

  Alternatively, by examining which pesticide is effective and selecting an effective pesticide for a specific plant, the pathogenic fungus harming the plant is preliminarily cultured, and the culture solution is used as a test sample 5. It can also be used.

  Examples of the phytopathogenic fungi targeted by the detection device of the present embodiment include tomato pathogenic fungi and fungi belonging to the genera Fusarium, Pyricularia, or Colletotrichum. Examples of the tomato pathogenic fungi include tomato gray mold (Botrytis cinerea), tomato soot mold (Pseudocercospora fuligena), and tomato leaf mold (Passalora fulva).

  Other examples of phytopathogenic fungi include Fusarium oxysporum, Pyricularia grisea, or Colletotrichum gloeosporioides. These phytopathogenic fungi cause root rot disease, blast, blast anthrax, gray mold, and the like. These phytopathogenic fungi kill plants. Examples of plant non-pathogenic fungi are Saccharomyces cerevisiae, Penicillium chysogeum, or Aspergillus oryzae.

  In the present embodiment, the target plant is preferably a tomato, and as the phytopathogenic fungi, tomato gray mold (Botrytis cinerea), tomato soybean mold (Pseudocercospora fuligena), and tomato leaf mold (Passalora) fluva).

  In the present specification, the term “plant pathogenicity” means having plant pathogenicity. The term “plant non-pathogenic” means not pathogenic to plants. If the fungus is pathogenic, but not pathogenic to the plant, then the fungus is "plant non-pathogenic". In other words, a fungus is "plant non-pathogenic" if it does not adversely affect the plant. The prefix "non" included in the term "plant non-pathogenic" does not modify "plant" and the prefix "non" modifies "pathogenic".

  In the detection device of the present embodiment, a culture solution 6 is stored in a culture solution storage section 4 provided below the artificial cell wall 2. The culture solution 6 is not particularly limited as long as it is a culture solution in which fungi can be cultured, and a general medium or culture solution can be used. For example, a potato dextrose medium, a sabouraud dextrose medium, and the like can be used. In order to accelerate the fungal culture, a culture solution may be added not only to the culture solution storage unit 4 but also to the test sample 5.

  In the present embodiment, the pesticidal drug substance is charged into the culture solution storage unit 4 together with the culture solution 6. As the pesticidal drug substance, different ones among a plurality of sensor-like devices included in the device are input. As the pesticide drug substance, any pesticide drug substance that is registered as a subject plant can be used without particular limitation.

  For example, kasugamycin (Keieiesuyujieiemuwaishiainhydrochloridemonohydrate), Mepapiniumu (Mepanipyrim), penthiopyrad (Penthiopyrad), triflumizole (triflumizole), difenoconazole (Difenoconazole), Fenpirazamin (Fenpyrazamine), iprodione (Iprodione), fludioxonil (Fludioxonil), tetrachloroisophthalonitrile ( TPN), iminoctadine albesylate (Iminoctadinealbesilate), captan (Captan), thiophanate-methyl (Tiophanete-methyl), benomyl (Benomyl), dietofencarb (Die) hofencarb), azoxystrobin (Azoxystrobin), it can be used polyoxin (Polioxin) or the like.

  In the detection device of the present embodiment, after a certain culture period, the phytopathogenic fungus in the sample is observed by observing whether or not phytopathogenic fungi appear on the back surface 23b of the cellulose membrane 23 of the artificial cell wall 2. The presence or absence of a fungus can be detected, and the presence or absence of a fungus can confirm whether the pesticide drug substance contained in the sensor-like device is effective.

  In order to perform the observation, the detection device of the present embodiment includes an optical observation unit 7 as shown in FIG. As the optical observation unit 7, an optical microscope or the like can be used.

  The culture period of the fungus is not particularly limited, but is preferably 24 hours or more, and more preferably 72 hours or more. Further, the culture temperature is preferably about 20 to 28 ° C.

  The detection device of the present embodiment includes a plurality of sensor-like devices 1 at least composed of a test sample input unit 3, an artificial cell wall 2, and a culture solution storage unit 4 as shown in FIG. The arrangement / arrangement of the plurality of sensor-like devices 1 is not particularly limited. For example, the plurality of sensor-like devices 1 may be arranged on a circumference as shown in FIGS. 3 to 5 or may be arranged in a block as shown in FIG. You can also. In the case of a block shape, dead space is created depending on the number of sensor-like devices to be arranged, and a large space is required to observe each block even if the detector moves or the sensor moves at the time of detection There is a problem as compared with the arrangement on the circumference shown in FIGS. 3 to 5, and thus it is considered that the arrangement on the circumference is more preferable.

  FIG. 3 is a top view when each sensor-like device 1 of the present embodiment is arranged on a circumference, and FIG. 4 is a perspective view thereof. FIG. 5 and FIG. 6 are schematic diagrams in a case where different pesticide active ingredients are charged into the respective sensor-like devices.

  In the present embodiment, it is preferable that the culture solution reservoir of at least one sensor-like device among the plurality of sensor-like devices does not contain the pesticide drug substance. For example, the sensor-like device shown as “blank” in FIGS. 5 and 6 does not include the pesticide drug substance. One or more blank sensor-like devices can be provided. It is preferable that the position of the blank sensor-like device be grasped and determined.

  As shown in FIG. 7, the detection device according to the present embodiment is configured such that the sensor-like devices arranged on the circumference rotate in the horizontal direction, and that the optical observation unit 7 is fixed to a specific place. When each sensor-like device 1 reaches the optical observation unit 7 by rotation, it is preferable to observe whether fungi appear from the bottom of the device upward.

  With such a configuration, only one optical observation unit 7 needs to be installed, and after that, the presence or absence of the fungus can be detected by rotating the sensor-like device.

  The detection (optical observation) is preferably started from the blank (no pesticide substance) device as shown in FIG. The blank is first observed, and if no hyphal growth is observed, the test sample can be determined to be free of pathogenic fungi and further detection can be stopped. Only when the growth of hyphae is recognized in the blank, thereafter, since it is possible to sequentially detect whether each pesticide active ingredient is effective, it is possible to suppress the detection time and the detection cost efficiently and efficiently. .

  There is no particular limitation on the order in which the pesticidal active ingredients are arranged, but it is preferable to prepare and arrange the pesticidal active ingredients with a low resistance risk. Specifically, for example, it is preferable to arrange TPN, captan, iminoctadine albesylic acid, fenpyrazamine, and fludioxonil in this order. After that, it is desirable to arrange a pesticide drug substance having a medium to high resistance risk (= resistant bacteria are likely to appear). Specifically, for example, kasugamycin, mepanipyrim, penthiopyrad, azoxystrobin, polyoxin, triflumizole, diphenoconazole, iprodione, thiophanate methyl, benomyl, and dietofencarb are preferably arranged in this order.

  The number of pesticide active ingredients differs depending on the target pathogenic fungus. For example, when only tomato soybean mold fungus (Pseudocercospora fuligena) is to be detected, as shown in FIG. It can be reduced to the number of types (right side in FIG. 8). It is considered that such a case in which the number of the pesticide active ingredients is reduced can be easily dealt with by arranging the sensor-like devices on the circumference instead of the blocks.

  Further, as shown in FIG. 9, the detection device of the present embodiment may include a liquid pool at the center of a concentric circle of the sensor-like device arranged on the circumference. By injecting the bacteria collection liquid (test sample) into the liquid pool, the test sample can be injected into each sensor-like device at once, which is efficient.

  Normally, when the devices are arranged as shown in FIG. 9 and the liquid pool is at the center, it is considered that the test sample automatically moves to each sensor-like device due to the capillary decrease. Alternatively, when the liquid pool at the center is higher than each sensor-like device, the flow path from the liquid pool to each sensor-like device can be inclined, so that the test sample moves to each sensor-like device by gravity. Further, when the disk on which the sensor-like devices are mounted is rotated, the test sample is distributed and injected from the liquid pool to each sensor-like device by centrifugal force.

  As still another embodiment, as illustrated in FIG. 10, the sensor-like device groups may be arranged several times, and a plurality of sensor-like devices may be arranged on each circumference. Further, it is preferable that a temperature partition block such as a heat insulating material is provided between the concentric circles, and a function of adjusting the temperature for each circle is provided. By doing so, there is an advantage that pathogenic fungi having different suitable growth temperatures can be simultaneously detected.

  For example, as shown in FIG. 11, a third region 300 can be provided as a heat insulating layer between the first region 100 and the second region 200. A temperature control unit (a first temperature control unit 101 and a second temperature control unit 201) is connected to each of the first area 100 and the second area 200, and is managed at different temperatures, so that a plurality of virulences having different suitable growth temperatures are obtained. Each can be at a suitable temperature for the fungus. As a result, a plurality of bacteria can be detected simultaneously. The temperature of each region does not need to be always constant. For example, the temperature is changed in the range of 20 to 25 ° C. in the first region 100 (the growth temperature of tomato gray mold and leaf mold), and 30 ° C. in the second region 200. And the control pattern may be constant (the growth temperature of tomato soybean mold fungus). The temperature range can be set appropriately according to the target fungus.

  The heat insulating layer is not particularly limited, and for example, a material such as rock wool can be used.

  As described above, according to the detection device of the present embodiment as described above, it is possible to present an effective pesticide from the growth state of the fungus in each sensor-like device. It is also considered that the type of pathogenic fungus can be specified from the effect pattern of the pesticide. Furthermore, by accumulating and organizing the data of the detection results, the appearance of resistant strains in fungi can be suggested.

(Method of detecting pathogenic fungi)
Furthermore, the present invention includes a method for detecting a phytopathogenic fungus, comprising selectively detecting a phytopathogenic fungus using the above-described detection device.

  The method for detecting a phytopathogenic fungus of the present embodiment is not particularly limited in other steps as long as the above-described detection device is used. For example, a step of introducing a test sample into the test sample input section 3 of the detection device A step of allowing the test sample to stand in the detection device (culturing step); a step of observing the back surface of the artificial cell wall 2 (cellulose membrane 23) of the detection device after standing; If a fungus is observed, determining that the test sample contains a phytopathogenic fungus.

(How to select pesticides)
In addition, the present invention includes a method for selecting an agricultural chemical, which uses the detection device as described above to select an effective active pharmaceutical ingredient.

The method for selecting a pesticide of the present embodiment is not particularly limited with respect to other steps as long as the above-described detection device is used.
A step of introducing different pesticide active ingredients into the culture solution storage units of a plurality of sensor-like devices other than the blank,
A step of introducing a test sample into the test sample input section 3 of the blank device, a step of allowing the test sample to stand in the detector (culturing step), and after standing, the artificial cell wall 2 (cellulose membrane 23) of the detector ) Observing the back side,
-If a fungus is observed on the back surface of the cellulose membrane 23, the test sample contains phytopathogenic fungi, and the device is rotated in the horizontal direction. Observing the back surface of the artificial cell wall 2 (cellulose membrane 23),
-When fungi are observed on the back surface of the cellulose membrane 23, it is determined that the pesticide input to the device is not effective, and when no fungi are observed, a step of determining that the pesticide is effective, It is preferable to include at least.

(Network configuration)
FIG. 12 is a diagram illustrating an example of a network configuration of an information processing system to which the sensor according to the embodiment of the present disclosure is applied. The information processing system includes a sensor detection unit 1100, a server 1200, a user terminal 1300, and an advisor terminal 1400. The information processing system is a system that notifies the detection of pathogenic bacteria of a crop (here, a tomato) from the measurement data of the sensor detection unit 1100, and notifies a user of the farmer of an expert's action, that is, advice of spraying a suitable pesticide. .

  The sensor detection unit 1100, the server 1200, the user terminal 1300, and the advisor terminal 1400 are communicably connected to each other via a network NT. The network NT is, for example, a network including an Internet communication network, a mobile phone communication network, and a public telephone line network.

  The server 1200 is configured by, for example, a cloud server including one or a plurality of computers. The server 1200 includes a processor such as a CPU and an FPGA, and a memory. The server 1200 acquires the measurement data of the sensor detection unit 1100 via the user terminal 1300, and stores it in the memory 1202 (FIG. 13).

  The user terminal 1300 is a terminal possessed by a user of a farmer who cultivates crops, and is configured by a portable information processing device such as a smartphone and a tablet terminal. Note that the user terminal 1300 may be configured by a stationary computer. The user terminal 1300 displays advice on pathogen control by the advisor transmitted from the advisor terminal 1400.

  The sensor detection unit 1100 according to the present invention detects the plant pathogen sensor, that is, detects the artificial cell wall penetrating hypha in the sensor block. Even if the detection is started simultaneously with the sensor installation, a predetermined time after the sensor installation is obtained. The detection may be started later. Alternatively, the detection may be repeated a plurality of times after the first detection and another predetermined time later. The user ID included in the measurement data is a user ID assigned to each sensor detection unit 1100.

  Although only one sensor detection unit 1100 is shown in FIG. 12 for convenience of description, this is an example, and a plurality of sensor detection units 1100 may exist. In this case, a plurality of sensor detection units 1100 may be provided for one farmhouse, or one or more sensor detection units 1100 may be provided for each farmhouse.

  In addition, the user terminal 1300 that relays the measurement data transmitted by the sensor detection unit 1100 is a user terminal 1300 possessed by the user of the farmhouse in which the sensor detection unit 1100 is installed. The communication address is stored. The user terminal 1300 that relays the measurement data transmits the measurement data transmitted from the sensor detection unit 1100 to the server 1200 in association with the user ID stored in advance in its own memory 1302 (see FIG. 13). Good. Thereby, the server 1200 can recognize which user's farm the measurement data is the measurement data transmitted from the sensor detection unit 1100 installed in the farm.

  Here, the measurement data has been described as being transmitted via the user terminal 1300, but this is an example. The sensor detection unit 1100 may directly transmit the measurement data to the server 1200. In this case, the sensor detection unit 1100 may store the user ID of the corresponding user in advance, and transmit the measurement data to the server 1200 in association with the user ID.

  The sensor detection unit 1100 and the user terminal 1300 are provided with an input unit 1105 and an input unit 1305, respectively. The plant pathogen sensor according to the present invention, other than the pathogen detection information of the crop, for example, removal / update of seedlings, sensor Information such as a disease that has already developed other than detection or information such as a medication history can be input. The above information is accumulated and organized in the server 1200, and becomes a disease occurrence / maintenance data table T104 in FIG.

  The advisor terminal 1400 is configured as an information processing terminal possessed by an advisor who gives various advices to the user. The information processing terminal may be, for example, a stationary computer, a notebook computer, or a mobile terminal such as a tablet terminal or a smartphone. Here, the advisor may be a pesticide maker that manufactures and sells agricultural chemicals, may be a maker of agricultural chemicals that manufactures and sells agricultural materials, and may be a consultant company that specializes in giving advice to users. Is also good. It should be noted that the adviser may be not only an organization such as a company, but also a person who works for advice. In addition, in individual fields / farms where users are scattered in the present invention, the advisor may be, for example, a central management department of a large agricultural corporation instead of an independent company.

  FIG. 13 is a block diagram showing an example of a detailed configuration of the information processing system shown in FIG. The server 1200 includes a data analysis unit 1201, a memory 1202, and a communication unit 1203. The data analysis unit 1201 is configured by a processor such as a CPU, for example. When the communication unit 1203 receives the measurement data transmitted from the sensor detection unit 1100 via the user terminal 1300, the data analysis unit 1201 stores the measurement data in the disease occurrence / maintenance data table T104 (see FIG. 14).

  The memory 1202 is composed of, for example, a semiconductor memory, and stores various tables shown in FIG. FIG. 14 is a diagram illustrating an example of the data configuration of various tables stored in the memory 1202 of the server 1200. The memory 1202 includes a user information table T101, an advisor information table T102, a measurement data table T103, and a disease occurrence / maintenance data table T104.

  The user information table T101 stores user information for identifying a user who receives advice from an advisor, and stores one piece of user information in one record. Specifically, the user information table T101 stores “user ID”, “area”, “address”, “mail address”, and “telephone number” in association with each other. “User ID” is an identifier for uniquely identifying a user. “Area” indicates a rough location of a farm where the user grows crops. "Address" is the address of the farm where the user grows the crop. "E-mail address" is the e-mail address of the user. "Phone number" is the telephone number of the user.

  The advisor information table T102 is a table that stores advisor information for identifying an advisor who has entered the information processing system, and stores one piece of advisor information in one record.

  Specifically, the advisor information table T102 stores “manufacturer name”, “area”, and “mail address” in association with each other. “Maker name” is the name of the advisor. "Area" is the approximate location of the advisor. "E-mail address" is the e-mail address of the advisor.

  The measurement data table T103 is a table that accumulates measurement data transmitted from the sensor detection unit 1100 in time series, and stores one measurement data in one record. Specifically, the measurement data table T103 stores “user ID”, “date / time”, “pathogen detection”, “effective pesticide”, “estimated bacterial species”, and “input items” in association with each other.

  The user ID is a user ID of a user who has installed the sensor detection unit 1100 that has transmitted the measurement data. “Date / Time” is the date and time of measurement of the measurement data. “Pathogen detection” is a result of pathogen detection measured by the sensor detection unit 1100, and is derived from the detection result of the blank section shown in FIG. 5 and the like in the present invention. In the present invention, this is derived from the detection result of the section containing the agrochemical substance shown in FIG. 5 and the like. The “estimated bacterial species” is input by the advisor in the light of the detection result of the section containing the agrochemical active substance, in comparison with the acquired data. The “input item” indicates an item input in the input unit 1105 of the sensor detection unit 1100.

  The disease occurrence / maintenance data table T104 includes, in addition to the past pathogen detection data, the onset disease or disease other than the sensor detection, for example, the disease that has already occurred and whose symptoms have been visually confirmed, other than the sensor detection, input from the input unit 1105 and the input unit 1305. This is a table for storing information on medication and information on cultivation schedules such as cultivation updates, etc., in which one record stores the other of the one piece of pathogen detection data.

  Referring back to FIG. The communication unit 1203 of the server 1200 includes a communication interface that connects the server 1200 to the network NT, and receives measurement data from the sensor detection unit 1100 via the user terminal 1300.

  The sensor detection unit 1100 includes a control unit 1101, a memory 1102, a detection unit 1103, and a communication unit 1104. The control unit 1101 is configured by, for example, a processor such as a CPU, and controls the entire control of the sensor detection unit 1100. For example, the control unit 1101 determines the presence or absence of a pathogenic bacterium by image processing from the image data acquired by the detection unit 1103, and calculates measurement data including information indicating in which section of the pesticide medium fungal penetration and growth of the fungus has been recognized. Generate.

  In addition, the control unit 1101 estimates a disease name from an appearance pattern indicating which of the agricultural chemical samples has a mycelium generated and which of the agricultural chemical samples has developed resistance among the plurality of predetermined agricultural chemical samples. Specifically, the memory 1102 stores in advance a disease identification table in which a disease name is associated with an appearance pattern. The control unit 1101 may estimate the disease by specifying which of the occurrence patterns stored in the disease identification table corresponds to the measured appearance pattern.

  The memory 1102 is, for example, a semiconductor memory, and stores a measurement data table T201 illustrated in FIG. FIG. 15 is a diagram illustrating an example of a configuration of the measurement data table T201 stored in the memory 1102 of the sensor detection unit 1100. The measurement data table T201 is a table that stores the measurement data of the sensor detection unit 1100 in chronological order, and stores one measurement data in one record. Specifically, the measurement data table T201 stores “date / time”, “pathogen detection”, “effective pesticide”, and “estimated bacteria type” in association with each other. Also, the information input from the input unit 1105 of the sensor detection unit 1100 is stored as “input items”.

  The “date / time”, “pathogen detection”, “effective pesticide”, “estimated bacterial species”, and “input items” are the same as the elements of the same name in the measurement data table T103 in FIG.

  Referring back to FIG. The detection unit 1103 of the sensor detection unit 1100 includes, for example, an image sensor, and acquires image data by capturing an image of the back surface of the artificial cell wall for each block of the plant pathogen sensor according to the present invention.

  The communication unit 1104 includes a communication interface that connects the sensor detection unit 1100 to the network NT. The communication unit 1104 transmits the measurement data to the server 1200 under the control of the control unit 1101, for example.

  The user terminal 1300 includes a control unit 1301, a memory 1302, a display unit 1303, a communication unit 1304, and an input unit 1305. The control unit 1301 is configured by a processor such as a CPU and controls the entire control of the user terminal 1300. The memory 1302 is composed of, for example, a semiconductor memory and stores various tables shown in FIG.

  FIG. 16 is a diagram illustrating an example of a data configuration of various tables stored in the memory 1302 of the user terminal 1300. The memory 1302 includes a user information table T301, an advisor information table T302, and a measurement data table T303. The user information table T301 stores the user information of the user having the user terminal 1300. The user information table T101 of FIG. 14 stores the user information of all the users who receive advice, while the user information table T301 of FIG. 16 stores only the user information of the corresponding user terminal 1300.

  The advisor information table T302 stores advisor information of an advisor who receives advice from a user having the user terminal 1300. The advisor information table T102 of FIG. 14 stores the advisor information of all the advisors who have entered, whereas the advisor information table T302 of FIG. 16 stores only the advisor information of the advisor who receives advice from the user of the user terminal 1300. .

  The measurement data table T303 is a table that stores measurement data measured by the sensor detection unit 1100 installed in the user who has the user terminal 1300. The measurement data table T103 in FIG. 14 stores the measurement data measured by all the sensor detection units 1100, whereas the measurement data table T303 in FIG. 16 stores the sensor detection data set by the user having the user terminal 1300. Only the measurement data measured by the unit 1100 is stored.

  Referring back to FIG. The display unit 1303 includes, for example, a display device such as a liquid crystal display, and displays various information under the control of the control unit 1301. Here, the display unit 1303 displays, for example, advice information transmitted from the advisor terminal 1400.

  The communication unit 1304 includes a communication interface that connects the user terminal 1300 to the network NT. The communication unit 1304 receives, for example, advice information transmitted from the advisor terminal 1400 and measurement data measured by the sensor detection unit 1100.

  The advisor terminal 1400 includes a control unit 1401, a memory 1402, a display unit 1403, and a communication unit 1404. The control unit 1401 is configured by a processor such as a CPU and controls the overall control of the advisor terminal 1400. The memory 1402 is, for example, a semiconductor memory, and stores various tables shown in FIG.

  FIG. 17 is a diagram illustrating an example of a data configuration of various tables stored in the advisor terminal 1400. The memory 1402 stores a user information table T501, an advisor information table T502, and a measurement data table T503.

  The user information table T501 stores the user information of the farmer user to whom the adviser gives advice. While the user information table T101 shown in FIG. 14 stores the user information of all the users who receive advice, the user information table T501 stores only the user information of the user to whom the adviser gives advice.

  The advisor information table T502 stores one piece of advisor information in one record. The advisor information table T102 illustrated in FIG. 14 stores the advisor information of all the entered advisors, whereas the advisor information table T502 stores only the advisor information of the corresponding advisor.

  The measurement data table T503 stores the measurement data measured by the sensor detection unit 1100 installed for the user to be advised. The measurement data table T103 shown in FIG. 14 stores measurement data of all users who receive advice, while the measurement data table T503 stores measurement data measured by the sensor detection unit 1100 corresponding to the user who gives advice to the corresponding advisor. Store only data.

(sequence)
FIG. 18 is a sequence diagram illustrating an example of processing of the information processing system illustrated in FIG. This sequence diagram is divided into an initial phase from S101 to S105 and a normal phase after S201. The initial phase is a phase for constructing disease occurrence / maintenance data. The normal phase is a phase in which the detection of pathogenic bacteria by the sensor detection unit 1100 in FIG. 12 is monitored using the disease occurrence / maintenance data constructed in the initial phase.

  In S101, the user terminal 1300 receives an input of user information. Here, the input user information includes “area”, “address”, “mail address”, and “telephone number” shown in the user information table T101 of FIG.

  In S102, the user terminal 1300 transmits the user information input in S101 to the server 1200. The server 1200 that has received the user information issues the user ID that uniquely identifies each user, and stores the user ID in the user information table T101 in association with the received user information.

  In S103, cultivation such as past disease occurrence, medication history, or seedling update of the user is input from the input unit 1105 of the sensor detection unit 1100 or the input unit 1305 of the user terminal 1300 installed on the farm of the user who has input the user information. Status data is entered. In S104, the sensor detection unit 1100 or the user terminal 1300 transmits the input data to the server 1200 in association with the user ID.

  In S105, the server 1200 generates disease occurrence / maintenance data based on the received user information data and stores it in the disease occurrence / maintenance data table T104 shown in FIG. Thus, the initial phase ends. Subsequently, the normal phase is started.

  In S201, the sensor detection unit 1100 measures measurement data. The measurement shown in S201 is repeatedly measured at intervals determined by the user. In S202, the sensor detection unit 1100 transmits the measurement data to the user terminal 1300. In S203, the user terminal 1300 transmits the measurement data to the server 1200 in association with the user ID. Further, in S204, the user terminal 1300 associates the input information such as the change in the cultivation status input to the input unit 1105 or the input unit 1305 with the user ID and transmits the user ID to the server 1200, and the data analysis unit 1201 of the server 1200 Then, the received measurement data is added to the disease occurrence / maintenance data table T104.

  When a pathogenic bacterium is detected from the transmitted measurement data, the detection of the pathogenic bacterium and the extraction of the active pesticide are converted into data in the processing of the server 1200 in S205, and the pathogenic bacterium detection information is transmitted to the advisor terminal 1400 in S206. You.

  In S207, the advisor terminal 1400 transmits advice information including advice for the user to the user terminal 1300. Here, the advice information is generated, for example, based on information input to the advisor terminal 1400 by a member of the advisor who has viewed the information on the detection of pathogenic bacteria and the effective pesticide. The destination of the advice information is the user terminal 1300 of the user having the user ID that detected the pathogenic bacteria.

  FIG. 19 is a flowchart illustrating an example of processing of the server 1200 in the normal phase. In S401, the communication unit 1203 of the server 1200 receives the measurement data transmitted from the user terminal 1300. In S402, the data analysis unit 1201 of the server 1200 processes the measurement data received in S401, and determines the presence or absence of a pathogenic bacterium. When the data analysis unit 1201 determines that a pathogenic bacterium is detected (YES in S402), the data analysis unit 1201 selects an effective pesticide from information indicating in which section of the pesticide culture medium the artificial cell wall penetration and growth was observed. (S403). In S404, the communication unit 1203 transmits the information indicating the detection of the pathogenic bacteria and the information indicating the effective pesticide selected in S403 to the advisor terminal 1400 as the pathogenic bacteria detection information (S404). On the other hand, if the determination in S402 is not pathogen detection (NO in S402), the process returns to S401.

(Screen diagram)
FIG. 20 is a diagram illustrating an example of a notification screen G100 displayed by the advisor terminal 1400 that has received the information regarding the disease occurrence risk. The notification screen G100 includes a disease-bacteria detection notification column R101, an active pesticide active substance display column R102, and a message column R103 to an advisor. The disease detection notification field R101 includes a user ID, a measurement date and time, and a disease detection message display field.

  The active pesticide display field R102 displays the active pesticide obtained by processing the measurement data by the server 1200. In the message field R103 to the adviser, a message such as "Please input the appropriate amount of pesticide and the recommended amount of spraying and send it" is displayed. The advisor sends the contents requested for display from the advisor terminal 1400. The sensor according to the present invention possesses the pesticide drug substance, and the commercially available pesticides are those having different names for the pesticide itself, and those having different drug substance concentrations, or those containing a plurality of pesticide drug substances. It is more appropriate for an advisor to make that choice. In the pesticide, an appropriate amount of application and an application interval are set for each crop, and the advisor inputs and transmits advice while referring to the disease occurrence / maintenance data table T104 in the memory 1202 of the server 1200. Further, if it is possible to estimate the species of the pathogenic bacteria detected from the types of the effective pesticide components, this is also included in the advice.

  21A and 21B are diagrams illustrating an example of the advice screen G200 displayed on the user terminal 1300 that has received the advice information. The advice screen G200 includes a pathogen detection notification column R201 and an advice column R202. The display content of the pathogen detection notification column R201 displays the same information as the column of the same name shown in the notification screen G100, and thus a detailed description is omitted. The display contents of the advice column R202 are the proper pesticide name, the amount of the applied pesticide, and the estimation of the detected pathogenic bacterium input by the advisor to the advisor terminal 1400.

  In the advice screen G200 of FIG. 21B, for example, this is a case where the adviser is a pesticide maker / salesperson, and further, a button B201 indicating “Click here to purchase pesticide” and a “suitable pesticide Button B202 that describes "spraying method" and a button B203 that describes "pesticide information". The button B201 is a button for the user to purchase the pesticide displayed in the advice column R202. When the button B201 is selected by the user, the user terminal 1300 displays a pesticide purchase screen. The user can purchase a pesticide by inputting information necessary for purchasing the pesticide on the pesticide purchase screen.

  The button B202 is a button for displaying a screen for introducing a specific method of spraying the pesticide displayed in the advice column R202. When button B202 is selected by the user, user terminal 1300 displays a screen for introducing a specific method of spraying the pesticide. This screen is, for example, a screen for introducing a more detailed spraying method than the spraying method of the pesticide displayed in the advice column R202. For example, a method of spraying a pesticide is introduced using a moving image or the like.

  The button B203 is a button for displaying detailed information of the pesticide displayed in the advice column R202. When button B203 is selected by the user, user terminal 1300 displays detailed information on the pesticide. Here, the detailed information includes information such as a disease for which the corresponding pesticide is effective, a disease that is not effective, a side effect, and a component.

  As described above, in the information processing system to which the sensor according to the present embodiment is applied, when a pathogenic bacterium is detected, the user can receive advice from an advisor. Therefore, the user can easily acquire a method of coping with the disease, and can suppress damage caused by the disease of the crop.

  Although the present specification discloses various aspects of the technology as described above, the main technology is summarized below.

  According to one aspect of the present invention, there is provided an apparatus for detecting a phytopathogenic fungus, comprising: an artificial cell wall; a test sample input section provided at an upper portion of the artificial cell wall; and a culture solution storage section provided at a lower portion of the artificial cell wall. A plurality of sensor-like devices having, and an optical observation unit for detecting hyphal growth in each sensor-like device, wherein the culture solution storage unit contains a different pesticide drug substance. Features.

  With such a configuration, it is possible to provide an apparatus and a method capable of easily and safely selectively detecting phytopathogenic fungi. In addition, it is possible to select an effective pesticide before the onset of the fungal disease, and it is possible to suppress labor for spraying useless pesticides, excessive medication, and the like.

  In the detection device, it is preferable that the sensor-like device is arranged on a circumference. Thereby, since the dead space is reduced and the observation efficiency is increased, it is considered that the above-described effects can be obtained more efficiently.

  Further, the artificial cell wall may have a through hole having a pore diameter of 2 to 7 μm, and at least include a substrate having a thickness of 5 to 150 μm, and a cellulose membrane having a thickness of 0.5 to 2 μm provided on one surface of the substrate. preferable. Thereby, it is considered that the above-described effects can be obtained more reliably.

  Further, in the detection device, it is preferable that the culture solution reservoir of at least one of the plurality of sensor-like devices does not contain the pesticide active ingredient. With such a configuration, when the test sample does not contain a pathogenic fungus, it can be determined at an early stage, and there is an advantage that subsequent useless tests can be omitted.

  Further, in the detection device, the sensor-like devices arranged on the circumference rotate in the horizontal direction, and the optical observation unit is fixed, and each sensor-like device is fixed in the optical observation unit. Is preferably observed from bottom to top. Thereby, it is considered that the above-described effects can be obtained more efficiently.

  Further, in the detection device, it is preferable that a liquid pool is provided at the center of a concentric circle of the sensor-like device arranged on a circumference, and a bacteria recovery liquid can be put into the liquid pool. Thereby, it is possible to save the trouble of putting the test sample into each device, and it is considered that the above-mentioned effects can be obtained more efficiently.

  Further, in the detection device, the sensor-like device group is arranged in multiple layers, a plurality of sensor-like devices are arranged on each circumference, and a temperature partition block is provided between concentric circles, It is preferable to have a function of adjusting the temperature. Thereby, there is an advantage that pathogenic fungi having different temperatures can be simultaneously detected.

  Further, in the detection device, the target plant is tomato, and the detection target is tomato gray mold fungus (Botrytis cinerea), tomato soybean mold fungus (Pseudocercospora fuligena), and tomato leaf mold fungus (Passalora fulva). It is preferably at least one selected from Thereby, it is considered that the above-described effects can be further exhibited.

  In addition, the pesticide drug substance contained in the culture solution reservoir may be kasugamycin hydrochloride monohydrate, mepapinium (Mepanipyrim), penthiopyrad (Pentthiopyon), fenfluzole, diflunozole, diflunozole, diflunozole, diflunozole, diflunozole, diflunozole, diflunozole, diflunozole, diflunozole, diflunozole, difluzole, difluzole, diflunozole, diflunozole, difluzole, difluzole, diflunozole, difluzole, difluzole, difluzole, diflunozole, difluzole, difluzole, difluzole, diflunozole, diflunozole, difluzole, difluzole, difluzole, difluzol, difluzol, difluzole, diflunozole). ), Fludioxonil, Tetrachloroisophthalonitrile (TPN), Iminoctadinealbesilate, Captan, Captan, Thiophanate-methyl, Benomyl yl), diethofencarb (Diethofencarb), azoxystrobin (Azoxystrobin), it is preferably at least one selected from the group consisting of polyoxin (Polioxin). Thereby, it is considered that the above-described effects can be further exhibited.

  Further, a method for detecting a phytopathogenic fungus according to another aspect of the present invention includes selectively detecting a phytopathogenic fungus using the above-described detection device.

  A method for selecting a pesticide according to still another aspect of the present invention includes selecting an effective pesticide active substance using the above-described detection device.

  Hereinafter, the present invention will be described more specifically with reference to Examples, but the scope of the present invention is not limited thereto.

[Example 1]
(Culture of Botrytis cinerea)
Botrytis cinerea, a kind of plant pathogen and a fungus of tomato gray mold, was inoculated on potato dextrose agar medium (Difco ™ Potato Dextrose Agar). The medium was then left for 1 week at a temperature of 25 degrees Celsius. Botrytis cinerea was given by Associate Professor Shimizu of Gifu University Faculty of Applied Biological Sciences. Next, a potato dextrose agar medium cultured with Botrytis cinerea, in which mycelia had sufficiently grown, was allowed to stand under black light irradiation for 4 days or more, and then allowed to stand at room temperature for 2 weeks or more to promote spore formation. Several ml of sterilized pure water was added dropwise to the Botrytis cinerea cultured potato dextrose agar medium that had been subjected to the above treatment, and the surface of the hypha was rubbed with a platinum loop, a brush or the like to obtain a mixed suspension of crushed mycelia / spores. Hereinafter, this aqueous solution is referred to as “phytopathogenic fungus aqueous solution 1”.

(Preparation of medium containing pesticide)
600 microliters of diluted potato dextrose broth containing agrochemicals (Difco ™ Potato Dextrose Broth) was added to the culture solution reservoir as a liquid medium (culture solution). Thus, a culture solution storage section containing a liquid medium was prepared.

  The diluted potato dextrose medium containing pesticides was prepared by diluting a potato dextrose medium prepared by a standard method 10 times with pure water, and the main components (pesticide drug substance) of the following various pesticides were prepared at a concentration 100 times the actual concentration. A DMSO (SIGMA) solution was prepared by adding and mixing 1/100 volume of diluted potato dextrose medium. The main components of the pesticides used in this example were Kasugamycin hydrochloride monohydrate (manufactured by Fuji Film Wako Pure Chemical Co., Ltd.), mepapinium (Mepanipirim) (manufactured by Fuji Film Wako Pure Chemical Co., Ltd.), and penthiopyrad (Penthipopyramide, SIGMA) ), Triflumizole (manufactured by Fujifilm Wako Pure Chemical Industries, Ltd.), difenoconazole (Difenoconazole) (manufactured by Fujifilm Wako Pure Chemical Industries, Ltd.), fenpyrazamine (Fenpyrazamine) (manufactured by Fujifilm Wako Pure Chemical Industries, Ltd.), Iprodion (Iprodion) (manufactured by Fuji Film Wako Pure Chemical Industries, Ltd.), Fludioxonil SIGMA-ALDRICH), tetrachloroisophthalonitrile (TPN) (manufactured by Fuji Film Wako Pure Chemical Industries, Ltd.), iminoctadine albesylate (Iminoctadinealbesilate) (manufactured by Fuji Film Wako Pure Chemical Industries, Ltd.), captan (Captan) (Tokyo Chemical) Industrial Co., Ltd. (TCI), thiophanate-methyl (SIGMA-ALDRICH), Benomyl (Fuji Film Wako Pure Chemical Co., Ltd.), Dietofencarb (Diethofencarb) (Fuji Film Wako Pure Chemical Co., Ltd.) ) And a commercial pesticide, not a pesticide main component, and Azoxystrobin (Amister 20 Flowable, manufactured by Syngenta Japan Co., Ltd.) Was used polyoxin (Polioxin) (polyoxin AL wettable powders, Kaken Pharmaceutical Co., Ltd.).

(Preparation of artificial cell wall)
The artificial cell wall in the detection device was prepared as follows.

  First, cellulose (obtained from SIGMA-ALDRICH, trade name: Avicel PH-101) was dissolved in an ionic liquid to prepare a cellulose solution having a concentration of 1%. The ionic liquid was 1-Butyl-3-methyl imidazolium chloride (obtained from SIGMA-ALDRICH).

  The cellulose solution was warmed to 60 degrees Celsius. Next, the cellulose solution was applied to the back surface of a container having a polyethylene terephthalate film on the bottom surface (obtained from Millipore Co., Ltd., trade name: Millicell PISP 12R 48) by spin coating at a rotation speed of 2000 rpm for 30 seconds. The polyethylene terephthalate film served as the substrate. The polyethylene terephthalate film had randomly a plurality of through holes with a diameter of 3 micrometers. Thus, a cellulose film having a thickness of 0.5 μm was formed on the back surface of the polyethylene terephthalate film. According to Millipore, the diameter of the through-hole may have an error of about ± 10%.

  The container was left at room temperature for 12 hours in ethanol. Thus, 1-Butyl-3-methyl imidazolium chloride was replaced with ethanol. In other words, 1-Butyl-3-methyl imidazolium chloride was removed from the cellulose film.

  Finally, the container was dried in a vacuum desiccator. Thus, the artificial cell wall to be tested in the present example was obtained.

(Preparation of detection device)
In Example 1, the sensor-like device shown in FIG. 1 was prepared as follows, in which the number of the pesticide active ingredients + 1, that is, 17 in total.

  That is, a container in which a cellulose film is formed on the back surface of the polyethylene terephthalate film (substrate) on the bottom surface, which is the artificial cell wall, is placed on a culture container (culture solution storage section) containing each of the above-mentioned pesticides, and different pesticide active ingredients are used. A sensor-like device containing was prepared. In addition, only one of the devices was a blank containing only DMSO. In each device, the back surface of the cellulose membrane was in contact with the liquid medium. Next, each device was horizontally arranged on the circumference, and first, an optical observation unit (microscope) was set below the blank device, and used as a device for detecting phytopathogenic fungi.

  Subsequently, water having a volume of 200 microliters was added to the inside of the container having the artificial cell wall of each device. Further, the aqueous solution 1 of phytopathogenic bacteria containing mycelium pieces and spores of 200 Botrytis cinerea cultured above was added to the inside of the container having the artificial cell wall.

  The container containing the aqueous phytopathogen solution was allowed to stand at a temperature of 25 degrees Celsius for 24 hours. In other words, in Example 1, the culture time was 24 hours.

  Thereafter, the number of mycelia of Botrytis cinerea that appeared on the back surface of the cellulose membrane of each sensor-like device was counted in order by visual observation through an optical microscope. When the pesticidal component shows an effect, no mycelium appears on the back surface of the cellulose membrane. FIG. 22 summarizes the results of Example 1.

(Discussion)
From the results in FIG. 22, among the pesticides subjected to the test, kasugamycin, polyoxin, iminoctadine albesylic acid, thiophanate methyl, and benomyl showed hyphae on the back side of the cellulose membrane, and these were pathogenic fungi of tomato gray mold. It has been shown that certain Botrytis cinereas are not effective pesticides. Other pesticides were confirmed to be effective.

[Example 2]
(Culture of Pseudocercospora fuligena)
Pseudocercospora fuligena, a fungal pathogen of tomato soybean mold, was inoculated on potato dextrose agar medium. The medium was then allowed to stand at a temperature of 28 degrees Celsius for one week. Pseudocercospora fuligena was obtained from the Genetic Resource Center, National Agriculture and Food Research Organization (MAFF No. 306728). Next, the Pseudocercospora fuligena hypha was transplanted from a potato dextrose agar medium to a burdock powder agar medium, and was further allowed to stand at a temperature of 28 degrees Celsius for 2-3 weeks. Then, mechanical stress such as rubbing with a brush or the like was applied. After that, the film was allowed to stand for at least 4 days under irradiation with black light and then at room temperature for 2 weeks or more to promote spore formation. A few ml of sterilized pure water was dropped onto the Pseudocercospora fuligena culture burdock powder agar medium that had been subjected to the above treatment, and the surface of the mycelium was rubbed with a platinum loop, a brush or the like to obtain a mixed suspension of crushed mycelia and spores. Hereinafter, this aqueous solution is referred to as “phytopathogenic fungus aqueous solution 2”.

  Into the container having the artificial cell wall, together with water having a volume of 200 microliters, an aqueous plant pathogenic bacterium solution 2 containing 200 mycelium pieces and spores of Pseudocercospora fuligena cultured above was added to the inside of the container having the artificial cell wall. A detection device was prepared in the same manner as in Example 1 except for the above.

  Thereafter, the same culture as in Example 1 was performed, and the number of mycelia of Botrytis cinerea that appeared on the back surface of the cellulose membrane of each sensor-like device was counted sequentially by visual observation through an optical microscope. When the pesticidal component shows an effect, no mycelium appears on the back surface of the cellulose membrane. FIG. 23 summarizes the results of Example 2.

(Discussion)
From the results in FIG. 23, among the pesticides used in the test, for mepapinium, penthiopyrad, polyoxin, and thiophanate methyl, hyphae appeared on the back surface of the cellulose membrane. Is not effective against In addition, although the number of kasugamycins was small, hyphae were confirmed, indicating that this was not an effective pesticide. Other pesticides were confirmed to be effective.

[Example 3]
(Culture of Passalora fulva)
Passalora fulva, a kind of plant pathogen and a fungus of tomato leaf mold, was inoculated on a potato dextrose agar medium. The medium was then left at a temperature of 23 degrees Celsius for 2-3 weeks. Passalora fulva was obtained from the Genetic Resource Center, National Agriculture and Food Technology Research Organization (MAFF No. 726744). Then, several ml of sterilized pure water was dropped onto Passalora fulva cultured potato dextrose agar medium in which the mycelia had sufficiently grown, and the surface of the mycelium was rubbed with a platinum loop, a brush or the like to obtain a mixed suspension of crushed mycelia / spores. Hereinafter, this aqueous solution is referred to as “phytopathogenic fungus aqueous solution 3”.

  Inside the container having the artificial cell wall, an aqueous solution 2 of phytopathogenic bacteria containing the mycelium pieces and spores of 200 Passalora fulva cultured above is added to the inside of the container having the artificial cell wall, together with water having a volume of 200 microliters. A detection device was prepared in the same manner as in Example 1 except for the above.

  Thereafter, the same culture as in Example 1 was performed, and the number of hyphae of Passalora fulva appearing on the back surface of the cellulose membrane of each sensor-like device was counted in order by visual observation through an optical microscope. When the pesticidal component shows an effect, no mycelium appears on the back surface of the cellulose membrane. FIG. 24 summarizes the results of Example 3.

(Discussion)
From the results in FIG. 24, among the pesticides subjected to the test, mepapinium, thiophanate methyl, and dietofencarb showed a considerable number of hyphae on the back surface of the cellulose membrane, and these pesticides are pathogenic fungi of tomato leaf mold. It was shown not to be effective against Passalora fulva. Also, diphenoconazole, fenpyrazamine, fludioxonil, and iminoctadine albesylic acid were found in a small number of mycelia, but it was also shown that these were not effective pesticides. And it was confirmed that pesticides other than the above are effective.

  The above examples show that according to the present invention, it is possible to selectively determine whether a test sample contains a phytopathogenic fungus and to present an effective pesticide for the phytopathogenic fungus.

  The device for detecting a phytopathogenic fungus of the present disclosure can selectively determine whether a test sample contains a phytopathogenic fungus and can present an effective pesticide against the phytopathogenic fungus before the onset of the fungal disease. . For this reason, the detection device of the present disclosure can be suitably used in various technical fields such as agriculture.

1: Sensor-like device 2: Artificial cell wall 3: Test sample input unit 4: Culture solution storage unit 5: Test sample 6: Culture solution 7: Optical observation unit 21: Substrate 22: Through hole 23: Cellulose film 23a: Cellulose film Front surface 23b: Cellulose membrane back surface 100: First region 101: First temperature control unit 200: Second region 201: Second temperature control unit 300: Third region 13: Communication unit 1100: Sensor 1101: Control unit 1102: Memory 1103 : Detection unit 1104: communication unit 1105: input unit 1200: server 1201: data analysis unit 1202: memory 1203: communication unit 1300: user terminal 1301: control unit 1302: memory 1303: display unit 1304: communication unit 1305: input unit 1400 : Advisor terminal 1401: Control unit 1402: Memory 1403: Display unit 1 04: communication unit B201: button B202: button B203: button G100: notification screen G200: advice screen NT: network R101: pathogen detection notification field R102: advice field R201: pathogen detection notification field R202: advice field T101: user information table T102 : Advisor information table T103: Measurement data table T104: Disease occurrence and maintenance data table T201: Measurement data table T301: User information table T302: Advisor information table T303: Measurement data table T501: User information table T502: Advisor information table T503: Measurement Data table

Claims (12)

Artificial cell wall, a plurality of sensor-like devices having a test sample input section provided at the upper part of the artificial cell wall, and a culture solution storage part provided at the lower part of the artificial cell wall,
It has an optical observation unit that detects hyphal growth in each sensor-like device,
Each of the culture solution reservoirs contains a different pesticide drug substance,
A device for detecting phytopathogenic fungi.   The detection device according to claim 1, wherein the sensor-like device is arranged on a circumference.   The artificial cell wall has a through hole having a pore diameter of 2 to 7 μm, and has at least a substrate with a thickness of 5 to 150 μm and a cellulose membrane with a thickness of 0.5 to 2 μm provided on one surface of the substrate. Or the detection device according to 2.   The detection device according to any one of claims 1 to 3, wherein the culture solution reservoir of at least one of the plurality of sensor-like devices does not contain the pesticide active ingredient. The sensor-like device arranged on the circumference rotates in the horizontal direction; and
The detection device according to any one of claims 2 to 4, wherein the optical observation unit is fixed, and the optical observation unit observes each sensor-like device from bottom to top. .   The detection device according to any one of claims 2 to 5, wherein a liquid pool is provided at a center of a concentric circle of the sensor-like device arranged on a circumference, and a bacteria recovery liquid can be charged into the liquid pool.   The sensor-like device group is arranged in several layers, a plurality of sensor-like devices are arranged on each circumference, and a temperature partition block is provided between concentric circles, and a function of adjusting the temperature for each circle is provided. The detection device according to any one of claims 2 to 6, which is provided.   The detection device according to any one of claims 1 to 7, wherein the target plant is a tomato.   The detection device according to claim 8, wherein the detection target is at least one selected from tomato gray mold fungus (Botrytis cinerea), tomato soot mold fungus (Pseudocercospora fuligena), and tomato leaf mold fungus (Passalora fulva).   The pesticide drug substance contained in the culture solution reservoir may be kasugamycin hydrochloride monohydrate, mepapinium (Mepanipyrim), penthiopyrad (Pentthiopyrad), triflumisol (triflunzole), diflunozole (pifluenzole), diflunozole (triflumizole), diflunoe (diflunozole). Fludioxonil, tetrachloroisophthalonitrile (TPN), iminoctadine albesylate (Iminoctadinealbesilate), captan (Captan), thiophanate-methyl, benomyl (Benomyl) , Diethofencarb (Diethofencarb), azoxystrobin (Azoxystrobin), at least one selected from the group consisting of polyoxin (Polioxin), detecting device according to any one of claims 1 to 9.   A detection method comprising selectively detecting a phytopathogenic fungus using the detection device according to any one of claims 1 to 10.   A method for selecting a pesticide, comprising selecting an effective pesticide active ingredient using the detection device according to claim 1.

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