A prediction method and system for winter wheat scab

Abstract

The present invention relates to the technical field of winter wheat disease monitoring, and in particular to a prediction method and system for fusarium head blight of winter wheat, comprising the following steps: S1. collecting the lesion area BM of N groups of winter wheat and the degree of grain deformation BC of N groups of winter wheat; S2. processing the collected lesion area BM of the N groups of winter wheat to generate the lesion expansion speed BS, and processing the lesion area BM of the N groups of winter wheat and the lesion expansion speed BS of the N groups of winter wheat to generate the average of the lesion area BM of winter wheat and the average of the lesion expansion speed BS of winter wheat. The present invention directly incorporates the lesion area, the degree of grain deformation and the lesion expansion speed data into the influence on the fusarium head blight of wheat, and specifically quantifies the influence degree, thereby improving the prediction accuracy of the fusarium head blight of winter wheat, and also real-time and quantitatively evaluates the degree of wheat suffering from fusarium head blight, and adopts differentiated remedial methods according to the degree of disease.

Description

Technical Field

[0001] The invention relates to the technical field of winter wheat disease monitoring, and in particular to a prediction method and system for winter wheat scab. Background Art

[0002] Wheat head blight, also known as ear rot, straw blight, rotten wheat head, red wheat head and red head miasma, is a disease of wheat caused by infection of various Fusarium fungi. It can occur from the seedling stage to the ear stage, causing seedling rot, stem base rot, stalk rot and ear rot, with ear rot being the most harmful. When the humidity is high, a pink mold layer can be seen on the diseased part. After wheat is infected, the thousand-grain weight decreases, the germination rate decreases, the germination potential weakens, the flour yield is low, the flour quality is poor, the color is gray, and the commercial value is reduced.

[0003] In the prior art, there is an application publication number CN115082797B for an early monitoring and early warning method and system for wheat fusarium head blight in a field environment, comprising: step one: a data acquisition module acquires image data with labeled data; step two: a model construction module preprocesses the image data, extracts characteristic wavelengths of the preprocessed image data, and constructs a wheat fusarium head blight image recognition model; step three: the monitoring module sets intelligent spore capturers at multiple monitoring points in a field environment to capture fusarium head blight pathogen spores, and when more than 10% of the monitoring points capture fusarium head blight pathogen spores and issue an early warning, a wheat fusarium head blight early warning detection area is determined according to the early warning area; step four: the recognition module uses a drone to take aerial photos of the wheat fusarium head blight early warning detection area, analyzes wheat image features through a wheat fusarium head blight image recognition model, and identifies diseased plants suffering from wheat fusarium head blight and the location distribution of the source of the disease, which is more convenient and efficient for wheat fusarium head blight monitoring and early warning, saving cost and time.

[0004] However, there are still the following deficiencies: From the above statements, it can be seen that by constructing a wheat fusarium rust image recognition model, wheat infected with fusarium rust is identified, and wheat fusarium rust is monitored and warned. However, the above scheme only identifies the diseased plants infected with wheat fusarium rust and the location distribution of the source of disease. It does not include directly affecting parameters such as the area of ​​lesions, the degree of grain deformation, and the speed of lesion expansion into the impact on wheat fusarium rust, nor does it include the area of ​​lesions, the degree of grain deformation, and the speed of lesion expansion into the quantification of the degree of impact on wheat fusarium rust. As a result, the prediction accuracy of winter wheat fusarium rust is reduced, and the degree of wheat fusarium rust cannot be evaluated in real time and quantitatively, and differentiated methods cannot be adopted according to the degree of disease. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a prediction method and system for winter wheat fusarium rust to solve the problems existing in the prior art.

[0006] Based on the above purpose, the present invention provides a method for predicting winter wheat scab, comprising the following steps:

[0007] S1. Collect the lesion area BM and grain deformation degree BC of winter wheat in group N;

[0008] S2. Processing the collected lesion areas BM of the N groups of winter wheat to generate lesion expansion speeds BS, and processing the lesion areas BM of the N groups of winter wheat and the lesion expansion speeds BS of the N groups of winter wheat to generate the average of the lesion areas BM of winter wheat and the average of the lesion expansion speeds BS of winter wheat;

[0009] S3. non-dimensionalizing the average of the winter wheat lesion area BM, the average of the winter wheat lesion expansion speed BS, and the winter wheat grain deformation degree BC to generate a winter wheat growth state coefficient XS, and comparing the winter wheat growth state coefficient XS with a preset winter wheat disease degree threshold value YZ to generate different evaluation signal levels;

[0010] S4. Execute different strategies according to the different evaluation signal levels.

[0011] Furthermore, the collected lesion area BM of the N groups of winter wheat was processed to generate the lesion expansion speed BS. The specific method is as follows: the average of the lesion expansion speed BS is obtained by dividing the sum of the lesion area differences by the sum of the time interval differences. The calculation formula is as follows:

[0012]

[0013] Furthermore, the calculation process of the winter wheat grain deformation degree BC is as follows: the long diameter and short diameter of the grain are measured by a particle size analyzer, and the calculation formula is as follows:

[0014]

[0015]

[0016] Among them, AR is a unitless value used to describe the relative shape of the particles. If AR is equal to 1, it means that the particles are round; if AR is greater than 1, it means that the particles are more slender; if AR is less than 1, it means that the particles are more round and short, CJ is the longest dimension of the particle, DJ is the shortest dimension of the particle, and C1 is the constant correction factor.

[0017] Furthermore, the lesion area BM of the winter wheat in the N groups and the lesion expansion speed BS of the winter wheat in the N groups are processed to generate the average of the lesion area BM of the winter wheat and the average of the lesion expansion speed BS of the winter wheat as follows:

[0018] The sets of the lesion areas BM of the N groups of the winter wheat and the lesion expansion speeds BS of the N groups of the winter wheat are as follows, where N is an integer greater than 1:

[0019] BM = [BM1, BM2... BM i ... BM N

[0020] BS = [BS1, BS2... BS i ... BS N

[0021] Wherein, BM i is the lesion area value of the winter wheat in the i-th group, and BS i is the lesion expansion speed value of the i-th group.

[0022] Furthermore, the calculation formulas for the averages of the lesion areas BM of the N groups of the winter wheat and the lesion expansion speeds BS of the N groups of the winter wheat are as follows:

[0023]

[0024]

[0025] Furthermore, the dimensionless treatment is performed on the average of the lesion areas BM of the winter wheat, the average of the lesion expansion speeds BS of the winter wheat, and the grain deformation degree BC of the winter wheat to generate the formula for the growth state coefficient XS of the winter wheat as follows:

[0026]

[0027] Wherein, α is the weight factor coefficient of the average of the lesion areas of the winter wheat, 0.2 ≤ α ≤ 0.4, β is the weight factor coefficient of the average of the lesion expansion speeds of the winter wheat, 0.2 ≤ β ≤ 0.4, γ is the weight factor coefficient of the grain deformation degree of the winter wheat, 0.2 ≤ γ ≤ 0.4, and C2 is a constant correction coefficient.

[0028] Furthermore, the process of comparing the growth state coefficient XS of the winter wheat with a preset disease degree threshold YZ of the winter wheat to generate different evaluation signals is as follows:

[0029] When 0 < XS ≤ YZ, a first evaluation signal is generated;

[0030] When YZ < XS ≤ 1.5YZ, a second evaluation signal is generated;

[0031] When 1.5YZ < XS ≤ 2YZ, a third evaluation signal is generated.

[0032] Furthermore, different strategies are executed according to the different evaluation signals, and the specific process is as follows:​​

[0033] When the first evaluation signal is generated, it indicates that the winter wheat does not have fusarium head blight, and the existing management method of the winter wheat is maintained;

[0034] When the second assessment signal is generated, it means that the winter wheat presents a mild case of fusarium head blight, and the infected wheat plants and grains should be removed to reduce the spread of pathogens in the field;

[0035] When the third evaluation signal is generated, it indicates that the winter wheat suffers from severe ergot disease and a strong fungicide should be sprayed.

[0036] A prediction system for winter wheat scab, comprising:

[0037] A data collection module is used to collect the diseased spot area BM of N groups of winter wheat and the grain deformation degree BC of N groups of winter wheat;

[0038] A data processing module is used to process the collected lesion areas BM of the N groups of winter wheat to generate lesion expansion speeds BS, and to process the lesion areas BM of the N groups of winter wheat and the lesion expansion speeds BS of the N groups of winter wheat to generate an average of the lesion areas BM of winter wheat and an average of the lesion expansion speeds BS of winter wheat;

[0039] A data analysis module, for performing dimensionless processing on the average of the winter wheat lesion area BM, the average of the winter wheat lesion expansion speed BS, and the winter wheat grain deformation degree BC to generate a winter wheat growth state coefficient XS, and compare the winter wheat growth state coefficient XS with a preset winter wheat disease degree threshold value YZ to generate different evaluation signal levels;

[0040] An execution module is used to execute different strategies according to the different evaluation signal levels.

[0041] Beneficial effects of the present invention:

[0042] By collecting the lesion area BM of N groups of winter wheat and the grain deformation degree BC of N groups of winter wheat, the collected lesion area BM of N groups of winter wheat is processed to generate the lesion expansion speed BS, and the lesion area BM of N groups of winter wheat and the lesion expansion speed BS of N groups of winter wheat are processed to generate the average of the lesion area BM of winter wheat and the average of the lesion expansion speed BS of winter wheat, and the average of the lesion area BM of winter wheat and the average of the lesion expansion speed BS of winter wheat and the grain deformation degree BC of winter wheat are dimensionless processed to generate the growth state coefficient XS of winter wheat, and the growth state coefficient XS of winter wheat is compared with the preset winter wheat disease degree threshold value YZ to generate different evaluation signal levels, and different strategies are executed according to different evaluation signal levels. Therefore, the present invention directly incorporates the lesion area, the grain deformation degree and the lesion expansion speed data into the influence on the scab of wheat, and specifically quantifies the influence degree, thereby improving the prediction accuracy of the scab of winter wheat, and also evaluates the degree of wheat scab in real time and quantitatively, and adopts differentiated remedial methods according to the degree of disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is the workflow diagram of the method of the present invention;

[0045] Figure 2 It is a working schematic diagram of the module unit of the present invention. DETAILED DESCRIPTION

[0046] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0047] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0048] Example 1

[0049] like Figure 1 As shown, the present invention provides a method for predicting winter wheat scab, comprising the following steps:

[0050] S1. Collect the lesion area BM and grain deformation degree BC of winter wheat in group N;

[0051] When the BM data of winter wheat lesion area changes, it will cause changes in the degree of winter wheat disease. The following are the specific reasons:

[0052] Pathogen spread: As the BM increases, the spread of pathogens (such as fusarium head blight fungi) within the plant or in the field will also increase. This means that more plant tissues may be infected, thereby increasing the overall disease severity.

[0053] Increased number of pathogens: An increase in the lesion area BM means that there are more infected tissues and cells, which can provide more opportunities for pathogens to survive and reproduce, leading to an increase in the severity of the disease.

[0054] Nutrient and water loss: Infected tissues are often unable to absorb and utilize nutrients properly and also lose water in the infected area. This leads to an imbalance in nutrient and water supply throughout the plant, which can increase the severity of the disease.

[0055] Decreased plant resistance: As the lesion area increases, the overall resistance of the plant may decrease. The immune system will be stressed and no longer effectively fight pathogens, making the disease more severe.

[0056] New sources of infection: Large areas of infected plants can act as new sources of infection, releasing more pathogens into the field environment, making other plants susceptible to infection, thereby increasing the severity of the disease.

[0057] Therefore, it is particularly important to measure the lesion area BM of winter wheat by using a lesion area measuring instrument. If this can be done, the following effects will be produced:

[0058] Early detection and early warning: The lesion area meter can help detect fusarium head blight early before it breaks out. This provides valuable time to take preventive and control measures to reduce the spread of the disease.

[0059] Accurate disease assessment: Fusarium head blight usually manifests as lesions, and measuring the area of ​​lesions BM can provide a more accurate disease assessment.

[0060] Tracking disease changes: By regularly measuring the lesion area (BM), the development and changing trends of fusarium head blight can be tracked. This helps predict the evolution of the disease and enables decision makers to better plan response strategies.

[0061] When the BC data of winter wheat grain deformation degree changes, it will cause changes in the degree of winter wheat disease. The specific reasons are as follows:

[0062] Impaired disease resistance: When winter wheat kernel deformation increases, it usually indicates that the overall health of the plant is poor. Poor plant health may lead to reduced disease resistance and susceptibility to pathogens.

[0063] Increased chances of pathogen invasion: Increased particle deformation BC may be accompanied by damage to plant tissues, such as deterioration of leaves, stems, roots, etc. This provides more opportunities for pathogens (such as fungi, bacteria or viruses) to invade plant tissues, thereby causing or aggravating diseases.

[0064] Accelerated disease spread: Changes in kernel deformation BC may affect the spatial configuration between winter wheat plants. If the kernel deformation BC increases, the distance between plants may decrease, making it easier for potential pathogens to spread, thereby accelerating the spread of diseases.

[0065] Insufficient nutrient supply: Changes in the degree of particle deformation BC may be related to the nutrient supply of the plant, such as water, nutrients, etc. Insufficient nutrient supply will affect the immune system in the plant, making it more vulnerable to pathogens.

[0066] Reduced coping capacity: Changes in kernel deformation BC may lead to impaired plant physiological conditions, making them less able to cope with diseases. Plants usually respond to pathogens through hormones, enzymes and other biochemical processes, which may be affected by changes in kernel deformation BC.

[0067] Therefore, it is particularly important to measure the grain deformation degree BC of winter wheat. If this can be done, the following effects will be achieved:

[0068] Early disease detection: Kernel deformation BC value can be used as one of the indicators for early disease detection. Fusarium head blight often causes deformation and damage to winter wheat kernels. By regularly measuring the kernel deformation BC value, farmers and agricultural professionals can identify kernel deformation and abnormal conditions early, thereby discovering the presence of Fusarium head blight earlier.

[0069] Epidemic prediction: By analyzing historical data and the BC value of kernel deformation, a model can be established to predict the potential risk of fusarium head blight. This helps farmers take preventive measures to reduce the possibility of disease outbreaks.

[0070] When the BS data of winter wheat lesion expansion speed changes, it will cause changes in the degree of winter wheat disease. The specific reasons are as follows:

[0071] Fast-spreading disease: A higher BS value generally means that the disease is able to spread faster to healthy plant parts. This causes the disease to spread wider and faster, resulting in a larger area of ​​infection. Therefore, a higher BS value generally results in a higher disease severity.

[0072] Impact on the disease development cycle: Changes in the lesion expansion rate BS can affect the development cycle of the disease. If BS increases, the disease may spread faster and enter the new growing season. This may lead to multiple infections in one season, further increasing the severity of the disease.

[0073] Increased agricultural losses: BS may lead to large areas of winter wheat being infected, thus increasing agricultural losses. This may include reduced yield, reduced quality and increased costs for treatment and disease control.

[0074] Therefore, it is particularly important to measure the lesion expansion rate BS. If this can be done, the following effects will occur:

[0075] Early epidemic monitoring: The measurement of the lesion expansion rate BS can help agricultural professionals and farmers monitor the spread of fusarium head blight early. A faster lesion expansion rate BS may indicate that the disease is spreading rapidly, which can serve as an early warning signal and prompt early control measures.

[0076] Evaluating disease-resistant varieties: By monitoring the lesion spread rate (BS) of different winter wheat varieties, their resistance to fusarium head blight can be evaluated. A lower lesion spread rate (BS) may indicate that some varieties are more resistant to the disease, so these varieties can be preferentially selected to reduce the spread of the disease.

[0077] To sum up, collecting the lesion area BM of winter wheat, the degree of grain deformation BC of winter wheat and the lesion expansion speed BS of winter wheat plays an extremely important role in predicting the disease severity of winter wheat. The following is a specific implementation method for collecting the lesion area BM of winter wheat, the degree of grain deformation BC of winter wheat and the lesion expansion speed BS of winter wheat in this implementation method.

[0078] S2. The collected lesion area BM of winter wheat in group N is processed to generate the lesion expansion rate BS. The specific method is as follows: the average lesion expansion rate BS is obtained by dividing the sum of the lesion area differences by the sum of the time interval differences. The calculation formula is as follows:

[0079]

[0080] The time interval between two adjacent data points (T N -T N-1 =T N-1 -T N-2 =…=T2-T1) are equal,

[0081] The calculation process of the grain deformation degree BC of winter wheat is as follows: the long diameter and short diameter of the grain are measured by a particle size analyzer, and the calculation formula is as follows:

[0082]

[0083]

[0084] Among them, AR is a unitless value used to describe the relative shape of the particles. If AR is equal to 1, it means that the particles are round; if AR is greater than 1, it means that the particles are more slender; if AR is less than 1, it means that the particles are more round and short, CJ is the longest dimension of the particle, DJ is the shortest dimension of the particle, and C1 is the constant correction factor.

[0085] Therefore, it means that the larger the BC value of the winter wheat grain deformation, the higher the degree of grain deformation; the smaller the BC value of the winter wheat grain deformation, the closer the shape of the grain is to the standard shape (usually round).

[0086] Usually 100 wheat kernels are selected and the number of kernels of each grade is converted into a percentage or proportion. This can be achieved by dividing the number of kernels of each grade by the total number of kernels. The grade of BC of the kernel deformation of winter wheat is determined according to the proportion. Among them, there are 15 slightly deformed kernels, 30 moderately deformed kernels and 55 severely deformed kernels. The above formula is used to calculate the following results:

[0087] The percentage of slightly deformed particles = (15 / 100)*100% = 15%;

[0088] The proportion of moderately deformed particles = (30 / 100) * 100% = 30%;

[0089] The proportion of severely deformed particles = (55 / 100)*100% = 55%.

[0090] Considering that wheat kernels with less than 15% deformation are slightly deformed, and those with more than 15% and less than 30% deformation are moderately deformed, you can classify the degree of deformation of winter wheat kernels into the following BC grades:

[0091] Slight deformation: BC ≤ 15%;

[0092] Moderate deformation: 15% <BC<30%;

[0093] Severe deformation: BC ≥ 30%.

[0094] You can also convert slight deformation, moderate deformation and severe deformation into numbers 1, 2 and 3 respectively, using the numbers 1, 2 and 3 to represent slight deformation, moderate deformation and severe deformation respectively. When a wheat field is evaluated during data collection, assuming that the grain deformation degree BC is found to be moderate deformation, then according to the above conversion rules, you can convert the evaluation result of the grain deformation degree BC of winter wheat into 2.

[0095] The process of processing the lesion area BM of N groups of winter wheat and the lesion expansion rate BS of N groups of winter wheat to generate the average of the lesion area BM of winter wheat and the average of the lesion expansion rate BS of winter wheat is as follows:

[0096] The set of the lesion area BM of N groups of winter wheat and the lesion expansion speed BS of N groups of winter wheat is as follows, where N is an integer greater than 1:

[0097] BM=[BM1、BM2…BM i …BM N ]

[0098] BS=[BS1, BS2…BS i …BS N ]

[0099] Among them, BM i is the lesion area value of winter wheat in group i, BS i is the expansion speed value of the lesion in the i-th group.

[0100] The calculation formula for the average of the winter wheat lesion area BM and the winter wheat lesion expansion speed BS is as follows:

[0101]

[0102]

[0103] S3. Nondimensionalize the average value of the lesion area BM of winter wheat, the average value of the lesion expansion speed BS of winter wheat, and the degree of grain deformation BC of winter wheat to generate the formula for the growth state coefficient XS of winter wheat as follows:

[0104]

[0105] Among them, α is the weight factor coefficient of the average value of the lesion area of winter wheat, 0.2 ≤ α ≤ 0.4, β is the weight factor coefficient of the average value of the lesion expansion speed of winter wheat, 0.2 ≤ β ≤ 0.4, γ is the weight factor coefficient of the degree of grain deformation of winter wheat, 0.2 ≤ γ ≤ 0.4, and C2 is the constant correction coefficient.

[0106] It can be seen from the above formula that within a certain range, when the average value of the lesion area BM of winter wheat, the average value of the lesion expansion speed BS of winter wheat, and the degree of grain deformation BC of winter wheat are higher, the growth state coefficient ZS of winter wheat is higher, and vice versa; when the average value of the lesion area BM of winter wheat, the average value of the lesion expansion speed BS of winter wheat, and the degree of grain deformation BC of winter wheat are lower, the growth state coefficient ZS of winter wheat is lower, and vice versa. Therefore, the average value of the lesion area BM of wheat, the average value of the lesion expansion speed BS of winter wheat, the degree of grain deformation BC of winter wheat, and the growth state coefficient ZS of winter wheat are all positively correlated.

[0107] Compare the growth state coefficient XS of winter wheat with the preset disease degree threshold YZ of winter wheat to generate different evaluation signals. The process is as follows:

[0108] When 0 < XS ≤ YZ, generate the first evaluation signal;

[0109] When YZ < XS ≤ 1.5YZ, generate the second evaluation signal;

[0110] When 1.5YZ < XS ≤ 2YZ, generate the third evaluation signal.

[0111] S4. Execute different strategies according to different evaluation signals. The specific process is as follows:

[0112] When the first evaluation signal is generated, it indicates that winter wheat has no scab, and the existing management method of winter wheat is maintained;

[0113] When the second evaluation signal is generated, it indicates that winter wheat has mild scab, and the infected wheat plants and grains should be removed to reduce the spread of pathogens in the field;

[0114] When the third evaluation signal is generated, it indicates that winter wheat has severe scab, and a strong fungicide should be sprayed.

[0115] A prediction system for winter wheat scab, such as Figure 2 As shown, including:

[0116] A data collection module is used to collect the diseased spot area BM of N groups of winter wheat and the grain deformation degree BC of N groups of winter wheat;

[0117] A data processing module is used to process the collected lesion areas BM of the N groups of winter wheat to generate lesion expansion speeds BS, and to process the lesion areas BM of the N groups of winter wheat and the lesion expansion speeds BS of the N groups of winter wheat to generate an average of the lesion areas BM of winter wheat and an average of the lesion expansion speeds BS of winter wheat;

[0118] A data analysis module, for performing dimensionless processing on the average of the winter wheat lesion area BM, the average of the winter wheat lesion expansion speed BS, and the winter wheat grain deformation degree BC to generate a winter wheat growth state coefficient XS, and compare the winter wheat growth state coefficient XS with a preset winter wheat disease degree threshold value YZ to generate different evaluation signal levels;

[0119] An execution module is used to execute different strategies according to the different evaluation signal levels.

[0120] Although the embodiments of the present invention have been shown and described, it is understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the attached claims and their equivalents. The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more available media sets. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium. The semiconductor medium may be a solid state hard disk.

[0121] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0122] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0123] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division of a waterway underwater terrain change analysis system and method. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0124] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0125] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0126] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0127] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0128] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting winter wheat scab, characterized in that: It includes the following steps: S1. Collect the lesion area BM of N groups of winter wheat and the degree of grain deformation BC of N groups of winter wheat; S2. Process the collected lesion area BM of the N groups of winter wheat to generate the lesion expansion speed BS, and process the lesion area BM of the N groups of winter wheat and the lesion expansion speed BS of the winter wheat to generate the average value of the lesion area BM of the winter wheat and the average value of the lesion expansion speed BS of the winter wheat; S3. Nondimensionalize the average value of the lesion area BM of the winter wheat, the average value of the lesion expansion speed BS of the winter wheat, and the degree of grain deformation BC of the winter wheat to generate the growth state coefficient XS of the winter wheat, and compare the growth state coefficient XS of the winter wheat with the preset disease degree threshold YZ of the winter wheat to generate different evaluation signal levels; S4. Execute different strategies according to the different evaluation signal levels; Process the collected lesion area BM of N groups of winter wheat to generate the lesion expansion speed BS. The following is the specific method: Divide the sum of the lesion area differences by the sum of the time interval differences to obtain the average value of the lesion expansion speed BS. The calculation formula is as follows: The calculation process of the degree of grain deformation BC of the winter wheat is as follows: Measure the major axis and minor axis of the grains through a particle size analyzer. The calculation formula is as follows: Where AR is a dimensionless value used to describe the relative shape of the particles. If AR is equal to 1, it means the particles are circular; if AR is greater than 1, it means the particles are more slender; if AR is less than 1, it means the particles are more round and short. CJ is the longest dimension of the particles, DJ is the shortest dimension of the particles, and C1 is a constant correction coefficient; The process of processing the lesion area BM of the N groups of winter wheat and the lesion expansion speed BS of the N groups of winter wheat to generate the average value of the lesion area BM of the winter wheat and the average value of the lesion expansion speed BS of the winter wheat is as follows: The sets of the lesion area BM of the N groups of winter wheat and the lesion expansion speed BS of the N groups of winter wheat are as follows, where N is an integer greater than 1: BM=[BM1、BM2…BM i …BM N ] BS=[BS1、BS2…BS i …BS N ] Among them, BM i is the lesion area value of winter wheat in group i, BS i is the expansion speed value of the lesion in group i; The calculation formula for the average value of the lesion area BM of the N groups of winter wheat and the average value of the lesion expansion speed BS of the N groups of winter wheat is as follows: The formula for nondimensionalizing the average value of the lesion area BM of the winter wheat, the average value of the lesion expansion speed BS of the winter wheat, and the degree of grain deformation BC of the winter wheat to generate the growth state coefficient XS of the winter wheat is as follows: Where α is the weight factor coefficient of the average value of the lesion area of the winter wheat, 0.2 ≤ α ≤ 0.4, β is the weight factor coefficient of the average value of the lesion expansion speed of the winter wheat, 0.2 ≤ β ≤ 0.4, γ is the weight factor coefficient of the degree of grain deformation of the winter wheat, 0.2 ≤ γ ≤ 0.4, and C2 is a constant correction coefficient.

2. A method for predicting winter wheat scab according to claim 1, characterized in that: The process of comparing the growth state coefficient XS of the winter wheat with the preset disease degree threshold YZ of the winter wheat to generate different evaluation signals is as follows: When 0 < XS ≤ YZ, generate the first evaluation signal; When YZ < XS ≤ 1.5YZ, generate the second evaluation signal; When 1.5YZ < XS ≤ 2YZ, a third evaluation signal is generated.

3. A method for predicting winter wheat scab according to claim 2, characterized in that: Different strategies are executed according to the different evaluation signals, and the specific process is as follows: When the first evaluation signal is generated, it indicates that the winter wheat has no scab, and the existing management method of the winter wheat is maintained; When the second evaluation signal is generated, it indicates that the winter wheat shows mild scab, and the infected wheat plants and grains should be removed to reduce the spread of pathogens in the field; When the third evaluation signal is generated, it indicates that the winter wheat shows severe scab, and a powerful fungicide should be sprayed.

4. A prediction system for winter wheat fusarium head blight, the system being used to execute a prediction method for winter wheat fusarium head blight according to any one of claims 1 to 3, characterized in that: Including: A data acquisition module for collecting the lesion area BM of N groups of winter wheat and the degree of grain deformation BC of N groups of winter wheat; A data processing module for processing the collected lesion area BM of the N groups of winter wheat to generate a lesion expansion speed BS, and processing the lesion area BM of the N groups of winter wheat and the lesion expansion speed BS of the N groups of winter wheat to generate the average value of the lesion area BM of the winter wheat and the average value of the lesion expansion speed BS of the winter wheat; A data analysis module for performing dimensionless processing on the average value of the lesion area BM of the winter wheat, the average value of the lesion expansion speed BS of the winter wheat, and the degree of grain deformation BC of the winter wheat to generate a growth state coefficient XS of the winter wheat, and comparing the growth state coefficient XS of the winter wheat with a preset disease degree threshold YZ of the winter wheat to generate different evaluation signal levels; An execution module for executing different strategies according to the different evaluation signal levels.

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