Early detection system for fungal pathogens and prediction of infection cycles in an agricultural plot
Patent Information
- Application Number
- FR2023008708
- Authority / Receiving Office
- FR · FR
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2023-08-14
- Publication Date
- 2025-02-21
Smart Images
Figure 00000000_0000_ABST
Abstract
Description
Title of the invention: System for the early detection of fungal pathogens and the prediction of infection cycles in an agricultural plot
[0001] The present invention relates to the technical field of phytopathology and the management of infectious diseases in agriculture. More specifically, the invention relates to systems for the early detection of fungal pathogens in an agricultural plot and for predicting the infectious cycles of said fungal pathogens in order to effectively treat associated plant diseases.
[0002] In the above-mentioned field, it is known that agricultural plots, and specifically those cultivated for grapes, are regularly affected by various pathogen epidemics, particularly fungal diseases, with downy mildew being a notable concern. This scourge afflicts grape plots, presenting a significant challenge for producers worldwide. The ubiquitous nature of downy mildew, its capacity for rapid proliferation, and its adaptation to diverse environmental conditions make the control and management of these epidemics difficult.
[0003] Downy mildew, a widespread fungal disease, spreads primarily through rainwater or irrigation, where fungal spores are released into the air and can be carried by the wind to plants. These spores germinate on plant leaves, forming filaments called hyphae that penetrate plant tissues and develop into white or gray infectious lesions. These infectious lesions release further spores that infect neighboring leaves. Without proper treatment, downy mildew can lead to plant death. A specific example is downy mildew of grapevines, a fungal disease that persists as oospores in leaves that fall to the ground during winter. The incubation period varies from 4 to 14 days depending on the temperature, with an optimum around 24°C. The germination of oospores and the development of mycelium require average temperatures above 11°C and rain.In spring, with the presence of free water and a temperature above 11°C, the oospores germinate and release zoospores that infect the young organs of the vine. These zoospores then differentiate into conidia which, in turn, infect the organs of the vine, repeating the cycle several times during the growing season.
[0004] Solutions have been proposed to combat fungal plant diseases. These include the use of fungicides, crop rotation, and plant genetic modification. However, these solutions are not without drawbacks. Indeed, the use of fungicides, although often effective, presents some There are considerable environmental and health risks. Furthermore, repeated use can lead to the emergence of resistant strains of pathogens, rendering treatments ineffective. On the other hand, crop rotation is not always applicable, especially in vineyards where vines are planted for several years. This method can also be ineffective if the pathogen has the ability to survive in the soil between two growing cycles.
[0005] As for the genetic selection and / or modification of plants, while it can certainly increase plant resistance to pathogens, it raises questions about its long-term safety as well as a number of ethical and environmental issues. Furthermore, this method requires advanced technology and significant financial investment, making this option inaccessible to many farmers.
[0006] Thus, current methods for managing these pathogens all present major drawbacks in terms of environmental and health impacts, ineffectiveness in certain situations, technological and financial constraints, and ethical concerns. Furthermore, they do not allow for the early and accurate detection of epidemics, making it difficult to implement effective preventive measures. In addition, these methods are not always applicable or effective on all types of crops and against all types of fungal pathogens.
[0007] There is therefore an urgent need for a solution that offers early and accurate detection of fungal pathogens and prediction of their associated primary and secondary infection cycles, that is applicable to a wide range of crops and effective against a broad spectrum of pathogens, while reducing environmental and health impacts, and that is technically and financially accessible to all agricultural producers.
[0008] There is therefore a need for a low-cost early detection system for fungal disease capable of predicting the primary and secondary infectious cycles of a pathogen present on an agricultural plot.
[0009] The invention therefore falls within this context and aims to resolve all the aforementioned drawbacks. Thus, the invention seeks to provide a system for the early detection of fungal diseases and the prediction of infection cycles in an agricultural plot comprising: a. fungal spore traps; b. a remote analysis unit enabling the detection of the presence of pathogenic fungal spores in fungal spore traps, the identification of the pathogen associated with said pathogenic fungal spores and the quantification of said pathogenic fungal spores; c. a modeling unit comprising a computer memory in which stores at least one mathematical model of infectious dynamics, the modeling unit being capable of receiving fungal disease detection data and meteorological data, characterized in that, following the detection of the occurrence of a fungal disease from said quantification of pathogenic spores, the remote analysis unit is capable of producing and transmitting fungal disease detection data to the modeling unit; and in that, in response to the reception of said fungal disease detection data, the modeling unit is arranged to: i. in the absence of prior prediction: establish, from said fungal disease detection data, at least one mathematical model of infectious dynamics and meteorological data, a prediction of primary and secondary infectious cycles for each identified pathogen; ii. where a prior prediction of infectious cycles is available: update, from said fungal disease detection data, at least one mathematical model of infectious dynamics and meteorological data, said prediction of infectious cycles.
[0010] By proceeding in this manner, the invention proposes to detect a fungal disease in an agricultural plot by capturing the fungal spores responsible for said disease. Furthermore, thanks to the remote analysis unit, it is possible to identify the pathogen at the source of said fungal disease. Thus, the modeling unit produces, based on at least one mathematical model calibrated from the combination of said fungal disease detection data and meteorological data, a prediction of the primary and secondary infection cycles of each identified pathogen. This makes it possible, in particular, to determine the optimal time for a treatment intervention and to predict action strategies according to the sporulation periods, especially those associated with the secondary infection cycles predicted by the model.
[0011] Advantageously, receiving a sample of fungal spores from the agricultural plot, collected by a spore trap, via a remote analysis unit allows for periodic data collection in the field. This offers a significant advantage in terms of responsiveness and accuracy, as the collected data directly reflects the condition of the field.
[0012] Furthermore, the detection and identification of at least one pathogen in the fungal spore sample by the remote analysis unit allows for rapid and targeted intervention. By identifying the specific pathogen responsible for the disease, the measures of controls can be adapted accordingly, which improves their effectiveness and minimizes damage to the crop.
[0013] Therefore, the production of fungal disease detection data and its transmission to the modeling unit allows for rapid and efficient information sharing.
[0014] Advantageously, the receipt of local meteorological data relating to the agricultural field by the modeling unit makes it possible to integrate environmental factors into the process of predicting infection cycles. This contributes to increased accuracy of the predictions, since meteorological conditions play a key role in the spread and development of fungal diseases.
[0015] Advantageously, establishing a prediction of primary and secondary infection cycles for each identified pathogen allows for a more accurate and detailed forecast of disease progression. By taking into account both primary and secondary infection cycles, a more complete picture of disease dynamics can be obtained, which can help in developing more effective management strategies. Furthermore, the ability to update a previous prediction based on newly collected data allows for the continuous refinement of predictions, thereby improving the accuracy and reliability of the entire system.
[0016] Advantageously, the remote analysis unit ensures enhanced accuracy in identifying fungal pathogens from fungal spores captured by fungal spore traps. Furthermore, the remote analysis unit determines the diversity of pathogens in the plot and quantifies, for each identified pathogen, the number of spores present on the analyzed sample. In addition, the modeling unit allows for the complete characterization of fungal spore maturity stages by, for example, generating a histogram of the maturity stages of the analyzed spore sample. Thus, thanks to the genetic signature of the pathogens, the analysis unit can identify potential known resistance to fungicide treatments for certain genetic variants of fungal pathogens.
[0017] Advantageously, the analysis unit generates fungal disease detection data comprising all the results of tests carried out on the sample from the fungal spore trap, which makes it possible to create a diagnosis at a given time in a format usable by the farmer himself and / or by a modeling unit for the purpose of predicting infectious cycles taking into account the most recent information.
[0018] Thanks to at least one mathematical model of infectious dynamics of fungal pathogens, the modeling unit can systematically establish a prediction of primary and secondary infectious cycles, thus capturing the dynamics of the progression of a fungal infection in the agricultural plot over the long term, This allows farmers to identify infection peaks and determine the optimal timing for treatment, aiming for disease eradication or, if the disease is not eradicable, to reduce losses. This is particularly useful for anticipating the spread of fungal diseases and proactively planning fungicide treatments, thereby reducing agricultural losses due to fungal infection.
[0019] Given the correlation between weather conditions and the development of fungal infections, incorporating real-time meteorological data significantly improves the accuracy of predictions of primary and secondary infection cycles made by the modeling unit. Furthermore, taking this meteorological data into account allows for a reassessment of the optimal time to begin a fungicide treatment, particularly in the event of heavy rain that could wash away the fungicide product and consequently negatively impact the expected effect of the treatment.
[0020] Advantageously, for the purpose of constructing a prediction of the primary and secondary infectious cycles of the fungal pathology, the modeling unit can integrate information concerning resistance to fungicidal treatments known for the identified genetic variants so as to improve the prediction made by at least one mathematical model.
[0021] In the context of the present invention, "primary infectious cycle" means the phase in the life cycle of a fungal pathogen during which oospores germinate and subsequently emit zoospores that emanate from the soil.
[0022] In the context of the present invention, the term "secondary infectious cycle" refers to the phase in the life cycle of a fungal pathogen during which spores released from infectious lesions infect other plant organs such as leaves, fruits, stems, and roots. Similarly, spores released from infectious lesions infect neighboring plants.
[0023] In one embodiment of the invention, said fungal spore traps are placed on the plot and are arranged to trap fungal spores in a regular manner and suitable for harvesting.
[0024] Advantageously, the placement of fungal spore traps on the agricultural plot allows fungal spores to be collected at any time of day, thereby facilitating continuous monitoring of the presence of fungal spores in the plot and early detection of fungal diseases.
[0025] Furthermore, fungal spore traps are passive elements for capturing said spores and therefore do not require electrical power, which reduces ancillary costs.
[0026] Moreover, the major advantage of these traps lies in their increased effectiveness, due in large part to part of their best mimicry of natural infection or spore exposure conditions per unit area. Unlike traditional spore capture methods, which can disrupt the natural environment and thus alter spore behavior, these passive traps are designed to more accurately mimic natural conditions. As such, they attract and capture spores more effectively, increasing the reliability of the data collected and providing a more precise overview of the distribution and prevalence of fungal spores in the studied environment.
[0027] Advantageously, in order to obtain detection of fungal spores with a particle size as fine as desired by the farmer, the fungal spore traps can be spatially arranged on the agricultural plot to create a spatial grid that covers the land in such a way as to maximize fungal spore capture.The traps can thus be arranged at different heights above the ground and at different latitudes and longitudes to form, for example, arrangements resembling Voronoi diagrams in 2 or 3 dimensions. The fungal spore traps are designed to be harvested by the system user; moreover, these fungal spore traps are arranged to allow the extraction of a sample containing the captured fungal spores.
[0028] If desired, said sample containing the captured fungal spores may be placed in a biological sample preservation medium, in particular a genetic material preservation tube comprising a preservation medium which may be, for example, one of: 70% ethanol, 95% ethanol, absolute ethanol, ethyl acetate, ethyl acetate and ethanol, ethylenediaminetetraacetic acid (EDTA), tris(hydroxymethylene)methanesulfonic acid (Tris-HCl), beta-mercaptoethanol or glycerol.
[0029] Advantageously, the remote analysis unit allows for precise analysis of the fungal spores captured by traps and, in particular, to identify the types of fungal pathogens present in the sample. The remote analysis unit is capable of performing analyses including, but not limited to, microscopic examination of fungal spores, enzyme immunoassays, fungal spore culture, as well as genetic techniques such as amplification sequencing and quantitative DNA and RNA sequencing and identification of the genetic signature of genetic sequences.
[0030] In an alternative embodiment of the invention, said fungal spore traps are portable, designed to be in contact with different parts of a plant, and arranged to trap fungal spores by adhesion.
[0031] Advantageously, this portable and adhesive configuration of fungal spore traps offers unparalleled flexibility and ease of use, allowing for rapid and efficient deployment in various areas of the agricultural plot. It promotes also the collection of more representative data on the presence and diversity of fungal spores, thus contributing to more accurate detection and proactive management of potential fungal infections.
[0032] In one embodiment of the invention, the analysis unit is capable of identifying pathogens present on a fungal spore trap from a biological diagnosis.
[0033] If desired, the biological diagnosis enabling the identification of pathogens may be one of the following: genetic sequencing or quantitative polymerase chain reaction.
[0034] If desired, the biological diagnosis enabling the identification of pathogens may be a molecular biology technique, in particular fluorescent in situ hybridization.
[0035] By proceeding in this way, the invention takes advantage of the accuracy and reliability of genetic techniques for the detection of pathogenic spores. Genetic sequencing, which relies on the analysis of DNA or RNA sequences present in spores, or on the examination of their physical structure, allows for a precise evaluation of fungal spores. This facilitates not only the detection and identification of the pathogen associated with the pathogenic fungal spores, but also their quantification. One of the main advantages of this approach lies in the fact that each pathogen can be identified by its unique genetic signature, thus avoiding misidentification, and allows for the precise determination of the pathogen's genetic variant, particularly in the context of targeted treatments for resistant variants.
[0036] Advantageously, the remote analysis unit is capable of isolating and preparing samples taken from collected traps for analysis, using techniques such as quantitative polymerase chain reaction (qPCR) and electrophoresis. Furthermore, if desired, the remote analysis unit may include a database containing information on pathogen specificities, such as all antifungal treatment resistances associated with each pathogen.
[0037] Compared to classical sequencing, quantitative polymerase chain reaction quantification is a method that is more sensitive and allows for much more precise pathogen detection compared with classical genetic sequencing and microscopy methods, which makes it possible to improve the accuracy of predictions of fungal infectious cycles significantly improved and more reliable.
[0038] In one embodiment of the invention, the modeling unit is arranged to enrich the model generated from the geographical and / or topographical data of the agricultural plot, data relating to the resistance to fungicide treatments of the detected disease, and the species and variety of crops present in the plot.
[0039] In one embodiment of the invention, the analysis unit is capable of identifying the pathogens present on a trap collected from at least one genetic sequencing technique, in addition to the detection of the presence of pathogenic fungal spores and the identification of the pathogen associated with said pathogenic fungal spores as well as the quantification of said pathogens.
[0040] By proceeding in this way, it is possible to achieve optimal accuracy and sensitivity, thus enabling better detection of fungal diseases and increased accuracy for the prediction of infectious cycles, which allows farmers and other sector managers to make informed decisions to reduce the prevalence of fungal disease in their agricultural plot.
[0041] In one embodiment of the invention, the system for predicting and detecting fungal disease early includes a genetic sequencing method which is a quantitative polymerase chain reaction.
[0042] Advantageously, quantitative polymerase chain reaction (qPCR) is a genetic sequencing method that is more sensitive and faster than older methods for detecting pathogenic fungal spores such as immunochromatographic tests, direct fungal cultures in the laboratory or identification by microscopic analysis.
[0043] Furthermore, the qPCR method is capable of detecting minute quantities of fungal spores and can be carried out in just a few hours (on the order of Ih), which is much faster than older methods, such as direct fungal cultures; this speed of analysis makes it possible to detect fungal diseases earlier, which is essential for effective treatment to better control fungal diseases and prevent their spread.
[0044] If desired, the quantitative polymerase chain reaction (QPCR) can be complemented by a second genetic sequencing technique such as next-generation sequencing (NGS). NGS allows for a more in-depth and detailed analysis of the pathogen's genome, which can help identify specific variants or mutations resistant to fungicides. This additional information can lead to a better understanding of the disease and can help guide the selection of the most appropriate treatment. Furthermore, NGS can be used to monitor the emergence of new resistant strains and to react quickly accordingly.
[0045] In one embodiment of the invention, the modeling unit is arranged to enrich the model generated from data of the agricultural plot, such as geographical and / or topographical data, with data relating to the resistance to fungicide treatments of the detected disease, and of the species and variety of crops in the plot.
[0046] By proceeding in this way, the enrichment of the model from said data allows a modeling of infection cycles more targeted on all the characteristics specific to each plot.
[0047] Advantageously, the modeling unit can be configured to enrich the generated model with data such as the size and shape of the agricultural plot, the number and type of crops, exposure to light or wind, plant varieties, and soil type. This increases the accuracy and level of detail in predicting primary and secondary infection cycles, thus enabling the correct timing of control measures.
[0048] In an alternative embodiment of the invention, the early detection system for fungal disease and the prediction of infectious cycles is equipped with fungal spore traps having an adaptable fastening system in order to fix them securely to a support in the agricultural plot.
[0049] Advantageously, the fastening system can include a variety of mechanisms, offering a wide range of possibilities for installing the fungal spore trap. This could include, but is not limited to, the use of hooks, suction cups, chains, wires, or ties made of synthetic materials that can be attached to various supports present in the agricultural plot, such as stakes or fence posts or palisade wires, to which the fungal spore trap can be fixed for a high position and maximum contact with the spore-carrying air current. Alternatively, adhesive tapes can be used to attach the plates containing the fungal spore traps to a surface, allowing for quick and easy installation.
[0050] In one embodiment of the invention, the system for early detection of fungal disease and prediction of infectious cycles in an agricultural plot includes a platform configured to receive an adhesive strip capable of capturing fungal spores.
[0051] Advantageously, said platform allows said adhesive strip to be received so as to facilitate its replacement in order to maintain optimal adhesion of fungal spores to said adhesive strip.
[0052] If desired, sensors to detect the presence of spores on the trap can also be implemented.
[0053] In one embodiment of the invention, the system also includes a collection tube containing a DNA preservative solution so that the spores captured in said adhesive strip can be safely preserved there for genetic sequencing analysis.
[0054] By proceeding in this way, the collection tube retains the fungal spores for a longer period, allowing scientists and farmers to carry out a more precise and detailed analysis of infection cycles. The use of a Preservative DNA solution also allows scientists and farmers to use more advanced analytical methods, such as genome sequencing, and gives them access to more accurate information on the type and quantity of pathogenic spores in a trap or on a plot.
[0055] In one embodiment of the invention, the modeling unit comprises a database of mathematical models of the dynamics of primary and secondary infectious cycles of fungal pathologies stored in computer memory, and is arranged to select the appropriate mathematical model according to the type of pathogen identified by the analysis unit.
[0056] Advantageously, the mathematical model base can include a multitude of models, each tailored to a specific fungal disease or group of fungal diseases. The mathematical models can be calibrated on various factors, such as the type of pathogen, the environmental conditions conducive to disease spread, the duration of the infectious cycle, and the susceptibility of the culture to the disease. This offers significant flexibility in the choice of model to use and allows for more accurate and tailored modeling of infectious cycles for each detected fungal disease.
[0057] If desired, the modeling unit can be programmed to automatically select the appropriate mathematical model based on the type of pathogen identified, for example, using an intelligent algorithm that compares the type of pathogen identified with a database of pathogens and their corresponding models stored in computer memory.
[0058] Furthermore, the modeling unit could be designed to update the mathematical model database based on new information received or new research on the infectious cycles of fungal diseases. For example, if a new fungal disease is identified or if additional information is obtained on the infectious cycle of an existing disease, a new model can be added to the database or an existing model can be adjusted accordingly, thus enabling the modeling unit to remain up-to-date with the latest research and to provide more accurate and current predictions.
[0059] Furthermore, the modeling unit can be configured to use several models in parallel if multiple pathogens are identified in the fungal spore sample. It can also be capable of combining the results of several models to provide an overall prediction of infection cycles, which is particularly useful in cases where an agricultural plot is affected by several fungal diseases simultaneously.
[0060] In one embodiment of the invention, the modeling unit comprises a graphical interface and the analysis unit is arranged to generate and transmit alerts via this graphical interface in case of detection of conditions favoring the development of fungal diseases, these conditions being determined on the basis of the infectious cycles predicted by said modeling unit.
[0061] Advantageously, the graphical interface could be a web application or a mobile application for a smartphone, allowing quick access to alerts and predictions of infection cycles. If desired, it could also be a local user interface that connects the modeling unit to another computer or system.
[0062] If desired, said analysis unit may generate and transmit not only alerts, but also information on measures to be taken to counter the spread of fungal diseases.
[0063] In one embodiment of the invention, the fungal spore traps are designed to protect the adhesive strip from weathering and phytosanitary treatments, in particular through the addition of a protective roof.
[0064] Advantageously, the presence of the protective roof ensures that the trap remains functional even in adverse weather conditions or after the application of plant protection products, which are commonly used in agricultural plots to combat various diseases and pests.
[0065] In one embodiment of the invention, at least one mathematical model of infectious dynamics is a stochastic process, in particular a Markov process, where the states represent the different phases of the infectious cycle of fungal diseases, and the transitions between the states are determined by probabilities calculated from spore capture data and other relevant environmental variables, thus providing a probabilistic representation of the infectious cycle.
[0066] Advantageously, the use of stochastic processes, and in particular Markov processes, in modeling infectious dynamics offers several benefits. By representing the different phases of the infectious cycle of fungal diseases by distinct states, and by determining the transitions between these states by probabilities calculated from spore capture data and other relevant environmental variables, the model offers a robust and adaptable representation of the complex reality of infectious cycles.
[0067] In particular, the fact that the transitions between states are determined probabilistically makes it possible to take into account the uncertainty inherent in the spread of fungal diseases. This can be particularly important in contexts where environmental conditions, which strongly influence the spread of fungal diseases, are subject to unpredictable fluctuations.
[0068] Furthermore, the use of a Markov process, in which the future state does not depend Focusing on the present state, rather than past states, can greatly simplify the model's complexity, thus facilitating its analysis and implementation. This property can prove particularly valuable in an agricultural context, where efficiency and speed of response can be crucial in combating the spread of fungal diseases.
[0069] Finally, the use of a stochastic process makes it possible to realistically simulate future disease spread scenarios, taking into account the uncertainties associated with infectious dynamics. This can provide farmers with valuable information for planning their actions, for example in terms of fungicide treatment or modification of cultivation practices, in order to optimally manage the risk of fungal disease.
[0070] In one embodiment of the invention, the early detection system for fungal disease and the prediction of infection cycles uses mathematical models based on ordinary differential equations to represent the rate of change of infection states in the agricultural plot. The state variables included in the equations may include the number of spores released, the host population density, and environmental conditions. The solutions to these equations can be obtained numerically to predict the infection cycle.
[0071] Advantageously, the use of models based on ordinary differential equations offers a mathematically rigorous approach to representing the rate of change of infectious states in the agricultural plot over time. By incorporating relevant variables such as the number of spores captured, the host population density, the life history traits of the population, and various environmental conditions, these models make it possible to grasp the complexity of the dynamic interactions underlying the infection cycles of fungal diseases. One of the strengths of the differential equation approach is its ability to represent continuous changes over time. It therefore offers a detailed and accurate description of the evolution of the infection, which can be of great help in understanding and anticipating disease progression.
[0072] Moreover, a large number of proven numerical techniques exist for solving these equations and obtaining solutions quickly and with high numerical accuracy.
[0073] Finally, the ordinary differential equations approach is flexible, easy to use, and adaptable. It allows for the easy integration of new variables or the adjustment of the equation coefficients to take into account new knowledge or changes in the conditions of the agricultural plot. This gives the detection and prediction system a capacity for continuous evolution and improvement, which can prove invaluable in addressing the challenges posed by the spread of diseases. fungal.
[0074] In one alternative or cumulative embodiment of the invention, the early detection system for fungal disease and the prediction of infectious cycles uses models based on partial differential equations to represent the spatial and temporal evolution of infectious states in the agricultural plot. The variables included in the equations may include the number of spores released, the spatial population density of the host, life history traits of the population such as mortality and fertility, as well as environmental conditions. The solutions to these equations can be obtained numerically to predict the spatial spread of the infection.
[0075] Advantageously, the use of models based on partial differential equations offers a mathematically rigorous approach to representing the evolution of infectious states in space and time. By incorporating relevant variables, these models make it possible to capture the complexity of the dynamic interactions that characterize the spread of fungal diseases. A major advantage of the partial differential equation approach is its ability to represent continuous changes in space and time, thus providing a detailed and accurate description of the spatial evolution of the infection.
[0076] In addition, a wide range of numerical techniques such as the finite element method and / or finite volume methods exist to solve these equations and obtain solutions quickly and with high numerical accuracy.
[0077] In one alternative or cumulative embodiment of the invention, the system for the early detection of fungal disease and the prediction of infectious cycles employs models based on stochastic differential equations. The variables integrated into these equations may include the number of spores produced at each time point, the density of the host population, and various environmental conditions.
[0078] One of the main advantages of the stochastic differential equation approach is its ability to represent continuous and random variations over time. It therefore offers a detailed and precise description of the evolution of the infection, which can be extremely useful for understanding and anticipating disease progression.
[0079] In one alternative or cumulative embodiment of the invention, the early detection system for fungal disease and the prediction of infectious cycles uses difference equation-based models to represent the evolution of infectious states in the agricultural plot at discrete time intervals.
[0080] The use of difference equation-based models offers a mathematically structured approach to representing the evolution of infectious states in the agricultural plot in discrete time. One of the major advantages of the equation-based approach One of its key differences is its ability to model changes occurring at defined time intervals, thus providing an accurate and detailed representation of infection evolution, which can be invaluable for understanding and predicting disease progression. Advantageously, the early detection system for fungal diseases and infection cycle prediction can include hardware and software components that capture and analyze data in real time. If desired, the remote analysis unit can be connected to a third-party database containing graphical and printed representations of fungal diseases and infection cycles, which can be displayed on a screen for easy visualization. Meteorological data can also be obtained from a dedicated station or remotely via an electronic network, if desired.If desired, the modeling unit can be incorporated into the remote analysis unit, while the mathematical model of infectious dynamics can take into account several environmental and biological factors, such as temperature, humidity, and pH.
[0081] The invention also relates to a method for early detection of fungal disease and prediction of infectious cycles in an agricultural plot, implemented by an early detection system for fungal disease and prediction of infectious cycles according to the invention, characterized in that it comprises the following steps: a. step of receiving, by the remote analysis unit, a sample of fungal spores collected by at least one spore trap; b. step of detection, identification and quantification of at least one pathogen present in said sample of fungal spores by the remote analysis unit; c. step of producing fungal disease detection data and transmitting said data to the modeling unit; d. stage of reception, by the modeling unit, of local meteorological data relating to the agricultural plot; e. step of establishing, by the modeling unit, a prediction of primary and secondary infectious cycles for each pathogen identified from the received fungal disease detection data, at least one predetermined mathematical model of infectious dynamics, the received meteorological data, and the existence of a previous prediction of infectious cycles by the modeling unit, in which the following sub-steps are executed: i. In the absence of previous prediction of infectious cycles: the modeling unit establishes, from said fungal disease detection data, at least one mathematical model of infectious dynamics and meteorological data, a prediction of primary and secondary infectious cycles for each identified pathogen; ii. in the case where a previous prediction of infectious cycles is available: the modeling unit updates, from said fungal disease detection data, at least one mathematical model of infectious dynamics and meteorological data, said previous prediction of infectious cycles.
[0082] In one embodiment of the invention, the method for early detection of fungal disease and prediction of infection cycles begins with the step of receiving, by the remote analysis unit, a sample of fungal spores collected by at least one spore trap. Next, the remote analysis unit performs the step of detecting and identifying at least one pathogen present in said sample of fungal spores. This step consists of generating fungal disease detection data and transmitting it to the modeling unit.Finally, the modeling unit, which also receives local meteorological data relating to the agricultural plot, is able, depending on the existence or not of a previous prediction of infectious cycles, to establish or update the step relating to the prediction of primary and secondary infectious cycles for each identified pathogen, from the fungal disease detection data received and the corresponding meteorological and mathematical data.
[0083] Advantageously, the step of detecting and identifying at least one pathogen present in the fungal spore sample may consist of genomic analysis. If desired, the step of receiving local meteorological data by the modeling unit may include receiving data relating to temperature, humidity, and precipitation levels. Advantageously, the step of establishing a prediction of infectious cycles by the modeling unit may consist of an adaptive mathematical algorithm that updates the prediction of infectious cycles in response to the meteorological and disease detection data received. If desired, the modeling unit may be configured to compare the sizes of fungal populations resulting from different infectious cycles in order to assess the effectiveness of fungal disease control.
[0084] The invention also relates to a fungal disease detection kit for an early detection system of fungal disease and prediction of infection cycles in an agricultural plot, according to the invention, comprising an adhesive strip adapted to collect fungal spores from the air and equipped with means for attaching to a fungal spore trap, as well as a container for the preservation of biological material adapted to receive at least a portion of said adhesive strip. Said container for the preservation of biological material contains a solution comprising a composition capable of preserving the integrity of fungal spores collected on the adhesive strip.
[0085] Advantageously, the container is hermetically sealed and contains an overpressure relief valve. If desired, a preservative can be added to the solution.
[0086] The invention also relates to a fungal spore trap for an early detection system for fungal disease and the prediction of infectious cycles, according to an embodiment described previously and comprising: a. a roof with an adaptable fixing system allowing the trap to be fixed to a support on an agricultural plot; b. a platform for attaching an adhesive strip provided with receiving elements for means of attaching said adhesive strip; said platform being fixed to a lower surface of the roof.
[0087] Advantageously, this specific design of the fungal spore trap offers numerous technical and operational benefits. The roof, with its adaptable fastening system, allows the trap to be attached to various supports found in an agricultural plot. This fastening flexibility greatly facilitates the installation of the trap, enabling its optimal placement according to the specific layout of the plot, the types of crops present, and the characteristics of the targeted pathogen.
[0088] The adhesive strip attachment platform, fixed to the underside of the roof, plays a crucial role in ensuring good exposure of the fungal spores to the adhesive strip. By allowing easy attachment of the adhesive strip, it also facilitates its replacement when necessary, making trap maintenance easier and more efficient.
[0089] In the context of this fungal spore trap, "adhesive tape" refers to any material or substance that has adhesive or sticky properties and that can be used to capture and retain fungal spores. This definition is broad enough to include a variety of materials and substances.
[0090] This can include conventional adhesive strips that are coated with a sticky material on one or both sides. It can also refer to more viscous substances such as petroleum jelly, which, due to its sticky nature, can effectively trap spores.
[0091] Furthermore, this may include materials specifically designed to capture particles of certain sizes, such as microporous filters. These filters may be coated with an adhesive substance to increase their spore-capturing capacity. In addition, the trap's roof provides weather protection for the adhesive strip, thus contributing to its durability and the trap's consistent effectiveness. It also protects the adhesive strip from plant protection products that may be applied in the field, thereby preventing possible contamination or deterioration of the strip.
[0092] Overall, this fungal spore trap design advantageously combines installation flexibility, ease of use and maintenance, and spore capture efficiency, thus contributing significantly to the overall performance of the system for early detection of fungal disease and prediction of infectious cycles.
[0093] In an alternative embodiment of the invention, the use of an adhesive strip with means for snapping the strip into grooves in the platform is envisaged. In this case, the adhesive strip is specially designed to attach securely in the grooves of the mounting platform. This ensures a secure and solid attachment of the strip, which is essential for the proper functioning of the fungal spore trap. Furthermore, the snap-on mechanism makes changing the strip quick and easy, which facilitates maintenance and improves the overall efficiency of the detection system. This design of the invention offers a robust and effective solution for trapping and detecting fungal spores in agricultural environments.
[0094] Advantageously, the first embodiment of the invention involves the use of an adhesive strip with means for snapping the strip into grooves in the platform. Indeed, this design offers a high level of flexibility and ease of use. The adhesive strip can be easily attached to the mounting platform by snapping it into the grooves designed for this purpose. This method ensures a solid and secure fit of the strip, thus preventing the risk of it coming loose due to external elements or movement. Furthermore, the strip replacement process is also simplified, since the user can simply detach the worn strip and replace it with a new one, thereby improving the effectiveness of the early detection system for fungal disease.
[0095] In another alternative embodiment of the invention, the use of a washer combined with double-sided adhesive tape is suggested. Part of the adhesive tape is attached to the washer, which is then fixed to the mounting platform. This provides additional fixation for the adhesive tape, ensuring increased stability under varying weather conditions. The choice of the washer and double-sided adhesive tape offers a resilient solution to temperature variations and extreme climatic conditions, thus ensuring reliable and efficient operation of the fungal spore trap. This embodiment offers additional flexibility in the design of the detection system, allowing the trap to be adapted to various agricultural environments and conditions.
[0096] Advantageously, this second embodiment of the invention offers a combination of a washer and double-sided adhesive tape. Part of the adhesive tape adheres to the washer, providing another way to attach the tape to the mounting platform. This option could be particularly beneficial in applications where These traps are ideal for applications where temperature fluctuations or extreme weather conditions could compromise the effectiveness of other fastening methods. Using double-sided adhesive tape ensures the strip remains securely attached to the platform, even in adverse weather conditions. Furthermore, the washer provides an additional stabilizing element, enhancing the safety and effectiveness of the fungal spore trap.
[0097] Advantageously, the roof of the fungal spore trap is substantially semi-cylindrical in shape. This specific design promotes the drainage of rainwater, thus preventing potentially harmful accumulation on the device's fungal spore-capturing adhesive strip. Furthermore, the semi-cylindrical shape of the roof helps direct the fungal spores toward the center of the trap, thereby increasing spore collection efficiency. Finally, the semi-cylindrical structure provides increased robustness to the trap, enabling it to withstand varying climatic conditions while maintaining its shape and functionality.
[0098] The invention also relates to a fungal spore trap for an early detection system for fungal disease and the prediction of infectious cycles, according to an embodiment described previously and comprising:
[0099] a. an adhesive strip suitable for trapping fungal spores;
[0100] b. a handle comprising a fastening system arranged to receive and to hold the adhesive strip during its application on various parts of a plant; Of course, the different features, variants and embodiments of the invention can be combined with each other in various combinations insofar as they are not incompatible or mutually exclusive.
[0101] In addition, various other features of the invention become apparent from the attached description made with reference to the drawings which illustrate non-limiting embodiments of the invention and where:
[0102] [Fig.1] represents, schematically and partially, a system for the early detection of fungal disease and the prediction of infectious cycles in an agricultural plot according to an embodiment of the invention.
[0103] [Fig.2] represents, schematically and partially, a method for predicting infectious cycles in an agricultural plot implemented by an early detection system for fungal disease and prediction of infectious cycles in an agricultural plot according to an embodiment of the invention.
[0104] [Fig.3] represents, schematically and partially, a fungal spore trap used in an early detection system for fungal disease and prediction of infectious cycles in an agricultural plot according to a first embodiment of the invention.
[0105] [Fig.4] represents, schematically and partially, a fungal spore trap of an early detection system for fungal disease and prediction of infectious cycles according to an embodiment of the invention.
[0106] [Fig.5] represents, schematically and partially, a fungal disease detection kit used in an early detection system for fungal disease and prediction of infectious cycles in an agricultural plot according to an embodiment of the invention.
[0107] [Fig.6] represents, schematically and partially, a graphical representation of the solution of a mathematical model of infectious dynamics used in a system for early detection of fungal disease and prediction of infectious cycles in an agricultural plot according to an embodiment of the invention.
[0108] [Fig.7] represents, schematically and partially, a spore trap used in a system for early detection of fungal disease and prediction of infectious cycles in an agricultural plot according to a second embodiment of the invention.
[0109] A system 100 for the early detection of fungal disease and the prediction of infectious cycles in an agricultural plot 19 comprising fungal spore traps 101, arranged to regularly trap fungal spores 17 and suitable for harvesting, is shown in [Fig. 1]; a remote analysis unit 102 for detecting the presence of pathogenic fungal spores 17 in the harvested fungal spore traps 101, said remote analysis unit 102 also for identifying the pathogen associated with said pathogenic fungal spores 17 and for quantifying them; a modeling unit 103 comprising a computer memory 107 in which is stored at least one mathematical model of infectious dynamics 108, the modeling unit 103 being capable of receiving fungal disease detection data and meteorological data 105.
[0110] Following the detection of the onset of a fungal disease from said quantification of pathogenic fungal spores 17, the remote analysis unit 102 is capable of producing and transmitting fungal disease detection data to the modeling unit 103; and, in response to the receipt of said fungal disease detection data, the modeling unit 103 is configured to: i. in the absence of previous prediction of infectious cycles: establish from said fungal disease detection data, from at least one mathematical model of infectious dynamics 108 and meteorological data 105, a prediction 109 of primary and secondary infectious cycles for each identified pathogen; ii. where a previous prediction of infectious cycles is available: update, from said fungal disease detection data, at least one mathematical model of infectious dynamics 108, of the topographic data of plot 106, fungicide treatment resistance data 104 and meteorological data, the aforementioned previous prediction of infectious cycles.
[0111] Each fungal spore trap 101 is connected to a remote analysis unit 102, which is shown as a separate building in the figure. Spore samples are sent to this remote analysis unit for accurate pathogen identification. The remote analysis unit includes advanced laboratory equipment for performing sophisticated tests and analyses on the received samples. Adjacent to the remote analysis unit is the modeling unit 103, shown as another building containing computer equipment. This unit receives fungal disease detection data from the analysis unit and local meteorological data, and then makes a prediction of primary and secondary infection cycles using one or more mathematical models of infection dynamics.Finally, the figure also illustrates a local weather station (4), which provides the meteorological data needed by the modeling unit. This station is equipped with various sensors to measure parameters such as temperature, humidity, rainfall, wind speed, and other environmental factors relevant to the development of fungal diseases.
[0112] Together, these elements of the system work in concert to achieve early detection of fungal diseases and effectively predict infection cycles in the agricultural plot.
[0113] Figure 2 shows a method for predicting infectious cycles in an agricultural plot implemented by an early detection system for fungal disease and for predicting infectious cycles in an agricultural plot according to an embodiment of the invention.
[0114] The process begins with a step 1 of reception, by the remote analysis unit 102, of a sample of fungal spores collected by at least one spore trap 101 placed at the level of the agricultural plot 19;
[0115] Step 1 is followed by a step 2 of detection and identification of at least one pathogen present in said sample of fungal spores by the remote analysis unit 102;
[0116] Step 2 is followed by a step 3 of producing fungal disease detection data and transmitting said data to the modeling unit 103;
[0117] Step 3 is followed by a step 4 of reception, by the modeling unit 103, of local meteorological data 105, relating to the agricultural plot 19;
[0118] Step 4 is followed by a step 5 in which the modeling unit 103 establishes a prediction of the primary and secondary infectious cycles for each pathogen identified from the fungal disease detection data received, of at least one mathematical model 108 of predetermined infectious dynamics, received meteorological data 105, and the existence of a previous prediction of infectious cycles by the modeling unit 103, in which the following sub-steps are executed:
[0119] Step 5 includes a first substep 51, in the absence of a previous prediction of infectious cycles: the modeling unit 103 establishes, from said fungal disease detection data, from at least one mathematical model of infectious dynamics 108 and meteorological data 105, a prediction of primary and secondary infectious cycles for each identified pathogen;
[0120] Step 5 includes a second substep 52, in the case where a previous prediction of infectious cycles is available: the modeling unit 103 updates, from said fungal disease detection data, from at least one mathematical model of infectious dynamics 108 and meteorological data 105, said previous prediction of infectious cycles.
[0121] Figure 3 illustrates a typical agricultural plot in which several fungal spore traps (101) of the system of Figure 1 are strategically positioned to efficiently capture spores from the environment. These traps are visible as small boxes (101) fixed to various supports in the plot (19). In the example described, the agricultural plot 19 includes a fence on which vines 18 are growing and on which fence wires 20 are laid. These fence wires 20 support the fungal spore traps 101 distributed at different heights and enabling the capture of fungal spores 17.
[0122] Figure 4 shows a fungal spore trap 101 of a system 100 for the early detection of fungal disease and the prediction of infectious cycles comprising: a. a semi-cylindrical roof 99, said roof includes an adaptable fixing system allowing the trap to be fixed on a support of an agricultural plot; b. a hooking platform 98 for an adhesive strip provided with receiving elements for hooking means 98 for said adhesive strip 97; said hooking platform 98 being fixed to a lower surface of the roof 99.
[0123] Figure 5 shows a fungal disease detection kit consisting of an adhesive strip 97 and a collection tube 201 containing a DNA preservative solution, in which the adhesive strip 97 collecting the fungal spores 17 can be placed for preservation.
[0124] In this case, the DNA preservation solution is absolute ethyl alcohol.
[0125] The numerical solutions of a mathematical model describing an infection caused by the oomycete Plasmopara viticola, and comprising, are shown in [Fig. 6]. stages of asexual and sexual reproduction. The mathematical model is a compartmental model based on a system of 4 ordinary differential equations, namely:
[0126] [Math.l] d5 1.— = aP — toS dt 2,— dP 4. — - gP - hZP dt Style: AppBody-Math
[0127] Where: a. S(t) is the population of sporangia at time t b. Z(t) is the zoospore population at time (t), c. O(t) is the oospore population at time (t), d. P(t) is the host plant population at time (t), e. a is the sporangium production rate per host plant (in sporangia / plant / hour), f. b is the sporangium loss rate (in 1 / hour), g. c is the rate of zoospore production per sporangium (in zoospores / sporange / hour), h. d is the zoospore loss rate (in 1 / hour), i. e is the rate of oospore production per zoospore (in oospores / zoospore / hour), j. f is the oospore loss rate (in 1 / hour), k. g is the reproduction rate of the host plants (in 1 / hour), 1. h is the rate of infection of host plants by zoospores (in plants / zoospore / hour).
[0128] The initial conditions for the populations of sporangia, zoospores, oospores and host plants are respectively S(0), Z(0), 0(0) and P(0).
[0129] In the system of differential equations we used to model the downy mildew life cycle, each parameter represents a specific rate in the life cycle. Here is a description of each parameter: a. a: This is the rate of sporangium production, which is proportional to the po host plant population. The unit of this parameter is the number of sporangia produced per host plant per unit of time (e.g., sporangia / plant / hour). b. b: This is the sporangium loss rate, which can be due to dispersal or mortality. The unit of this parameter is the inverse of time (for example, 1 / hour). c. c: This is the rate of zoospore production from the sporangia. The unit of this parameter is the number of zoospores produced per sporangium per unit of time (e.g., zoo spores / sporangium / hour). d. d: This is the zoospore loss rate, which can be due to attachment to the host plant or mortality. The unit of this parameter is the inverse of time (e.g., 1 / hour). e. e: This is the rate of oospore production from zoospores. The unit of this parameter is the number of oospores produced per zoospore per unit of time (e.g., oospores / zoospore / hour). f. f: This is the oospore loss rate, which can be due to germination or mortality. The unit of this parameter is the inverse of time (e.g., 1 / hour). g. g: This is the reproduction rate of the host plants. The unit of this parameter is the inverse of time (for example, 1 / hour). h. h: This is the infection rate of host plants by zoospores. The unit of this parameter is the number of plants infected by zoospore per unit of time (e.g., plants / zoospore / hour).
[0130] The solution of the mathematical model was obtained by a numerical resolution based on the Runge-Kutta algorithm of order 4 and the solutions were represented in the graph of [Fig.6].
[0131] In our model, the parameters were defined as follows: the sporangium production rate a is 0.308, the sporangium loss rate b is 0.271, the zoospore production rate is 0.505, the zoospore loss rate d is 0.489, the oospore production rate e is 0.255, the oospore loss rate f is 0.085, the host plant reproduction rate g is 0.702, and the host plant infection rate h is 0.149. The initial conditions for the sporangium, zoospore, oospore and host plant populations are respectively a. S(0) = 0.55; b. Z(0) = 0; c. O(0) = 0; and d. P(0) = 10.
[0132] The last [Fig.6] shows the evolution of susceptible vine populations, infected and resistant vines, as well as the oospore population, are plotted over time. The curves corresponding to each compartment are shown on the same graph, allowing for a direct visual comparison of the dynamics of each population. The x-axis represents time, while the y-axis represents the number of vines or oospores.
[0133] Figure 7 shows a spore trap 101 used in an early detection system for fungal disease and the prediction of infection cycles in an agricultural plot 19 according to a second embodiment of the invention and comprising:
[0134] a. an adhesive strip 97 suitable for trapping fungal spores 17;
[0135] b. a 500 handle comprising an adaptable fastening system (not shown) allowing the adhesive strip to be held firmly in place when applied to various parts of a plant.
Claims
Claims
1. System (100) for early detection of fungal disease and prediction of infectious cycles in an agricultural plot (19) comprising: a. fungal spore traps (101); a remote analysis unit (102) for detecting the presence of pathogenic fungal spores (17) in the harvested fungal spore traps (101), said remote analysis unit (102) also for identifying the pathogen associated with said pathogenic fungal spores (17) and for quantifying said pathogenic fungal spores (17); b. a modeling unit (103) comprising a computer memory (107) in which at least one mathematical model of infectious dynamics (108) is stored, the modeling unit (103) being capable of receiving fungal disease detection data and meteorological data (105); characterized in that, following the detection of the occurrence of a fungal disease from said quantification of pathogenic fungal spores (17), the remote analysis unit (102) is capable of producing and transmitting fungal disease detection data to the modeling unit (103); and in that, in response to the reception of said fungal disease detection data, the modeling unit (103) is arranged to: i. in the absence of previous prediction of the infectious cycles: establishing, from said fungal disease detection data, at least one mathematical model of infectious dynamics (108) and meteorological data (105), a prediction (109) of the primary and secondary infectious cycles for each identified pathogen; ii. in the case where a previous prediction of the infectious cycles is available: updating, from said fungal disease detection data, at least one mathematical model of infectious dynamics (108) and meteorological data (105), said previous prediction of the infectious cycles.
2. System for early detection of fungal disease and prediction of infectious cycles in an agricultural plot (19) according to claim 1, characterized in that the analysis unit (102) is capable of identifying the pathogens present on a trap (101) collected from at least one biological diagnostic technique.
3. System for early detection of fungal disease and prediction of infectious cycles in an agricultural plot (19) according to claim 2, characterized in that the biological diagnostic technique comprises at least one genetic sequencing technique.
4. System for early detection of fungal disease and prediction of infectious cycles in an agricultural plot (19) according to claim 1 to 3, characterized in that the modeling unit (103) is arranged to enrich the model generated from geographical and / or topographical data (106) of the agricultural plot, data relating to the resistance to fungicidal treatments of the detected disease, and the species and variety of crops present in the plot.
5. System for early detection of fungal disease and prediction of infectious cycles in an agricultural plot (19) according to one of the preceding claims, characterized in that the fungal spore traps (101) are provided with an adaptable fixing system allowing them to be fixed on a support of the agricultural plot.
6. System for early detection of fungal disease and prediction of infectious cycles in an agricultural plot (19) according to one of the preceding claims, characterized in that the fungal spore traps (101) comprise a platform (98) configured to receive an adhesive strip (97) capable of capturing fungal spores (17).
7. System for early detection of fungal disease and prediction of infectious cycles in an agricultural plot (19) according to one of the preceding claims, characterized in that the modeling unit (103) comprises a base of mathematical models of dynamics of the primary and secondary infectious cycles of fungal pathologies stored in the computer memory (107); and in that the modeling unit (103) is arranged to select the mathematical model (108) on the basis of the type of pathogen identified by the analysis unit (102).
8. System for early detection of fungal disease and prediction of infectious cycles in an agricultural plot (19) according to one of the preceding claims, characterized in that the modeling unit (103) comprises a graphical interface, and in that the analysis unit (102) is arranged to generate and transmit via this graphical interface alerts in the event of detection of conditions favoring the development of fungal diseases, these conditions being determined on the basis of the infectious cycles predicted by said modeling unit (103).
9. System for early detection of fungal disease and prediction of infectious cycles in an agricultural plot (19) according to one of the preceding claims, characterized in that the at least one mathematical model of infectious dynamics (108) is a stochastic process, in particular a Markov process, where the states represent the different phases of the infectious cycle of fungal diseases, and the transitions between the states are determined by probabilities calculated from the spore capture data and other relevant environmental variables, thus providing a probabilistic representation of the infectious cycle.
10. System for early detection of fungal disease and prediction of infectious cycles in an agricultural plot (19) according to one of the preceding claims, characterized in that the at least one mathematical model is based on differential equations to represent the rate of change of infectious states in the agricultural plot, the variables in these equations may include the number of spores captured, the population density of the host, the environmental conditions, among others, and the solutions of these equations may be obtained numerically to predict the infection cycle.
11. Method for early detection of fungal disease and prediction of infectious cycles in an agricultural plot (19), implemented by a system (100) for early detection of fungal disease and prediction of infectious cycles according to one of the preceding claims, characterized in that it comprises the following steps: a. step (1) of reception, by the remote analysis unit (102), of a sample of fungal spores collected by at least one spore trap (101) arranged at the agricultural plot (19); b. step (2) of detection, identification and quantification of at least one pathogen present in said fungal spore sample by the remote analysis unit (102); c. step (3) of producing fungal disease detection data and transmitting said data to the modeling unit (103); d. step (4) of receiving, by the modeling unit (103), local meteorological data (105), relating to the agricultural plot (19); e. step (5) of establishing, by the modeling unit (103), a prediction of the primary and secondary infectious cycles for each pathogen identified from the received fungal disease detection data, at least one predetermined mathematical model (108) of infectious dynamics, the received meteorological data (105), and the existence of a previous prediction of the infectious cycles by the modeling unit (103), in which the following sub-steps are executed: i. (51), in the absence of previous prediction of the infectious cycles: the modeling unit (103) establishes, from said fungal disease detection data, at least one mathematical model of infectious dynamics (108) and meteorological data (105), a prediction of the primary and secondary infectious cycles for each identified pathogen; ii. (52), in the case where a previous prediction of the infectious cycles is available: the modeling unit (103) updates, from said fungal disease detection data, from at least one mathematical model of infectious dynamics (108) and meteorological data (105), said previous prediction of the infectious cycles.