Digital management system and method for wheat scab prediction model

Abstract

The invention discloses a digital management system and method for a wheat scab prediction model, and relates to the technical field of digital big data, and the method comprises the steps: collecting wheat growth meteorological environment data in an observation region period through a meteorological collection device, and collecting wheat images in the observation region period through an image collection device; based on the collected environment data, a wheat growth environment data matrix in a corresponding period is constructed by taking the collection time point as a data label; performing diseased ear rate analysis on the wheat environment data of the current period in combination with historical wheat lesion environment data; abnormal pixel point positioning processing is carried out on the collected image in combination with the pixel point chromaticity information of the wheat historical image; performing wheat region division by combining abnormal pixel points of the current wheat image, performing lesion feature analysis by combining divided sub-regions, and predicting lesion sub-regions; and performing visual output on the diseased ear rate analysis and region division information of the current wheat growth region, and performing warning on the predicted diseased region.

Description

Technical Field

[0001] The present invention relates to the field of digital big data technology, and in particular to a digital management system and method for a wheat scab prediction model. Background Art

[0002] Wheat fusarium head blight prediction uses multiple factors such as meteorological data, crop growth conditions, and historical disease occurrence data. Through statistical analysis, machine learning and other methods, an analysis model is established to predict the occurrence trend and severity of wheat fusarium head blight. By analyzing the data, early warning of the disease can be given to reduce disease losses.

[0003] At present, wheat may be affected by fusarium ergot at multiple stages in its growth cycle. Accurate prediction of the occurrence of fusarium ergot during the wheat growth cycle is an important means to protect wheat growth. In the current model, most wheat fusarium ergot detection relies on manual judgment based on human experience and full-cycle monitoring. However, this detection method has large errors in accuracy and cannot accurately locate large-scale wheat production areas. There are large prediction range errors and accuracy errors. Summary of the invention

[0004] The object of the present invention is to provide a digital management system and method for a wheat scab prediction model to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A digital management method for a wheat scab prediction model, the method comprising the following steps:

[0007] S100, collecting meteorological environment data of wheat growth in the observation area period by using meteorological collection equipment, and simultaneously collecting wheat images in the observation area period by using image collection equipment;

[0008] S200, based on the collected environmental data, construct a wheat growth environment data matrix in the corresponding period with the corresponding collection time point as the data label; analyze the diseased ear rate of wheat environmental data in the current period by combining historical wheat disease environment data;

[0009] S300, performing abnormal pixel location processing by combining the collected image with the pixel chromaticity information of the wheat historical image; dividing the wheat area based on the abnormal pixel points of the current wheat image, and performing pathological feature analysis based on the divided sub-areas to predict the pathological sub-areas;

[0010] S400: Visually output the diseased ear rate analysis and regional division information of the current wheat growing area, and issue a warning for the predicted diseased area.

[0011] The specific steps of S100 for collecting meteorological environmental data of wheat growth in the observation area period by using meteorological collection equipment and synchronously collecting wheat images in the observation area period by using image collection equipment are as follows:

[0012] S101, collecting real-time wheat growth environment data through meteorological equipment of the wheat growth area device; obtaining wheat growth environment data within the corresponding period by setting a collection period; transmitting the collected data to the analysis end by using regional networking; the meteorological equipment includes a temperature sensor, a humidity sensor, a light sensor, etc.; the wheat growth environment includes temperature, humidity, light, wind force, etc.;

[0013] S102, collecting images of the entire wheat growing area through an image collection device, and remotely transmitting the collected image data to an analysis end through an airborne communication device; the image collection device is a high-precision image sensor.

[0014] The S200 constructs a wheat growth environment data matrix in a corresponding period based on the collected environmental data with the corresponding collection time point as the data label; the specific steps of analyzing the diseased ear rate of wheat environmental data in the current period by combining the historical wheat disease environment data are as follows:

[0015] S201, retrieve the wheat growth environment data collected in the period, and classify the collected environment data types by determining the collection time point; construct a corresponding environment type data set for the environment data collected at each time point; use the collection time point as a matrix data label, coordinate the environment type data sets collected at each time point in the period in chronological order, and construct a period wheat growth environment data matrix;

[0016] S202, retrieve the wheat growth environment data matrix of the current observation area, extract the environmental data corresponding to each environmental type in the environmental data set at each time point in the matrix, and perform periodic comprehensive analysis on each type of environmental data. The calculation formula is:

[0017]

[0018] Wherein, PVE(n:T) is the period equilibrium value of the environment data with type number n in the corresponding period T; M(t∈T) is the number of time points t in the period T; t∈T is the time point t in the period T; H(n:t) is the value of the environment data with type number n at each time point t in the corresponding period T;

[0019] Based on the periodic equilibrium value of various types of environmental data within the period, the fusarium head blight rate of wheat observed within the period was analyzed, and the calculation formula is:

[0020]

[0021] Wherein, YDP(T) is the fusarium head rate of wheat observed in period T; Fn is the fusarium head rate influence coefficient of the environmental data corresponding to type number n; by retrieving the historical environmental data of wheat fusarium head rate, and taking the period equilibrium value of the corresponding types of environmental data of the current period as a reference, the fusarium head rate data of wheat in the corresponding period with the same historical environmental data are retrieved; by performing error analysis on the retrieved historical fusarium head rate data and the fusarium head rate of the current period, if |YDP(T)-YDP(AL)|>E, the current observed wheat environmental data are recollected and the above environmental data analysis steps are performed again; wherein YDP(AL) is the mean value of the historical fusarium head rate under the same environmental data of wheat in the current period; E is the error control parameter;

[0022] If |YDP(T)-YDP(AL)|≤E, the comprehensive impact ratio of the diseased ear rate of the currently observed wheat is analyzed, and the calculation formula is:

[0023]

[0024] Among them, POI(T) is the comprehensive impact ratio of the diseased ear rate of wheat observed in the current cycle; YDP(Amax) and YDP(Amin) are the maximum and minimum values ​​of the historical fusarium head blight rate data under the same environmental data of wheat in the current cycle, respectively.

[0025] The S300 performs abnormal pixel location processing by combining the collected image with the pixel color information of the wheat historical image; divides the wheat area based on the abnormal pixel points of the current wheat image, and performs pathological feature analysis based on the divided sub-areas. The specific steps of predicting the pathological sub-area are as follows:

[0026] S301, retrieve the growth image data of wheat in the region, and obtain the chromaticity data of each pixel in the image by magnifying the local window of the image; the chromaticity data is the RGB three-channel intensity value corresponding to the pixel; the coordinates of each pixel in the image are acquired by establishing a coordinate system, and the chromaticity data of the corresponding pixel is associated with the coordinate data of the corresponding pixel by constructing a data association channel; the association process is to obtain the corresponding chromaticity data by retrieving the coordinate information corresponding to the pixel; the corresponding chromaticity feature vector is constructed based on the chromaticity data corresponding to each pixel in the coordinate system, and the feature vector of each pixel is combined with the standard chromaticity feature vector of historical healthy wheat for screening and analysis, and the calculation formula is:

[0027]

[0028] Among them, D[(x,y):HE] refers to the similarity between the chromaticity feature vector of the pixel point with coordinates (x,y) and the standard chromaticity feature vector of historical healthy wheat; is the chromaticity feature vector of the pixel corresponding to the coordinate (x, y); is the standard chromaticity feature vector of historical healthy wheat; and The chromaticity feature vector of the pixel corresponding to the coordinate (x, y) and the modulus of the standard chromaticity feature vector of historical healthy wheat; the vector dot multiplication is used for calculation between vectors; the chromaticity feature vector of the corresponding coordinate is Among them I R ,I G ,I B The chromaticity intensity values ​​of the R, G, and B channels corresponding to the coordinate pixel points respectively; by setting the health parameter Dp, if D[(x, y):HE]≤Dp, the pixel point corresponding to the current coordinate is judged to be a normal pixel point; if D[(x, y):HE]>Dp, the pixel point corresponding to the current coordinate is judged to be an abnormal pixel point; the abnormal pixel points are explicitly marked, and the normal pixel points are screened out;

[0029] S302, based on the wheat area coordinate image data after pixel point screening, equal area division is performed, and labels are assigned to each sub-area; by analyzing the similarity of the chromaticity feature vectors of the abnormal feature points in each sub-area with the standard lesion abnormal pixel points of the historical fusarium rust wheat, the calculation formula is:

[0030]

[0031] Among them, D[(x,y) j :SC] refers to the similarity between the chromaticity feature vector of the pixel point with coordinate (x, y) in the corresponding sub-region number j and the chromaticity feature vector of the standard abnormal lesion pixel point of the historical scab disease wheat; is the chromaticity feature vector of the pixel with coordinate (x, y) in the corresponding sub-region number j, is the chromaticity feature vector of the standard abnormal lesion pixel of historical fusarium rust wheat, is the modulus of the chromaticity feature vector of the pixel with coordinate (x, y) in the corresponding sub-region number j, is the modulus of the chromaticity feature vector of the standard abnormal lesion pixel of the historical fusarium head blight wheat;

[0032] Combining the similarity analysis data of the chromaticity feature vectors corresponding to the abnormal pixels in each sub-region with the standard chromaticity feature vectors of historical healthy wheat and the chromaticity feature vectors of the standard abnormal pixels of lesions in historical fusarium rust wheat, the fusarium rust abnormality index of wheat in the sub-region was analyzed, and the calculation formula was:

[0033]

[0034] Among them, GI jis the abnormal index of wheat fusarium wilt in the corresponding sub-region numbered j; f is the activation function; U j is the number of outliers in the sub-region numbered j; U a is the number of abnormal points in the whole area of ​​the wheat image; D[(x,y) j :HE] is the similarity between the chromaticity feature vector of the pixel with coordinate (x, y) in the corresponding sub-region number j and the standard chromaticity feature vector of historical healthy wheat; b is the bias parameter; (x, y)∈j is the coordinate of each pixel in the sub-region number j; by introducing the contrast index GI(th), if GI j <GI(th), then it is judged that there is no risk of fusarium fusarium in the current sub-region corresponding to number j; if GI j ≥GI(th), it is judged that the wheat in the current sub-region corresponding to number j is at risk of fusarium ergot; if there are multiple sub-regions with the risk of fusarium ergot, the monitoring priority is allocated from large to small according to the corresponding fusarium ergot abnormality index of each sub-region.

[0035] The specific steps of S400 for visually outputting the diseased ear rate analysis and regional division information of the current wheat growing area and warning the predicted diseased area are as follows:

[0036] S401, visually output the diseased ear rate analysis data of the currently observed wheat growth area, the regional image pixel point analysis data, and the regional division data;

[0037] S402. Provide warning feedback to sub-areas with risk of ergot disease and conduct monitoring based on the priority of each sub-area.

[0038] A digital management system for a wheat scab prediction model, the system comprising a regional data acquisition module, a periodic diseased ear rate analysis module, a regional abnormal index analysis module and a prediction output module;

[0039] The regional data acquisition module collects meteorological environment data of wheat growth within the observation area period through meteorological acquisition equipment, and simultaneously uses image acquisition equipment to collect wheat images within the observation area period; the periodic diseased ear rate analysis module constructs a wheat growth environment data matrix within the corresponding period based on the collected environment data, with the corresponding collection time point as the data label; the diseased ear rate of wheat environment data in the current period is analyzed by combining historical wheat pathological environment data; the regional abnormal index analysis module performs abnormal pixel location processing by combining the collected image with the chromaticity information of the pixel points of the historical wheat image; the wheat area is divided according to the abnormal pixels of the current wheat image, and the pathological characteristics are analyzed in combination with the divided sub-areas, and the pathological sub-areas are predicted; the prediction output module visualizes the diseased ear rate analysis and regional division information of the current wheat growth area, and warns the predicted pathological area.

[0040] The regional data acquisition module includes a regional environment data acquisition unit and a wheat image data acquisition unit;

[0041] The regional environmental data acquisition unit collects real-time wheat growth environment data through the meteorological equipment of the wheat growth area device; obtains the wheat growth environment data within the corresponding period by setting the acquisition cycle; and transmits the collected data to the analysis end by using the regional networking;

[0042] The wheat image data acquisition unit acquires images of the entire wheat growing area through an image acquisition device, and remotely transmits the acquired image data to an analysis end through an airborne communication device.

[0043] The periodic diseased ear rate analysis module includes an environmental data processing unit and a wheat diseased ear rate analysis unit;

[0044] The environmental data processing unit retrieves the wheat growth environment data collected periodically, and classifies the collected environment data according to the corresponding collected environment data type by determining the collection time point; constructs a corresponding environment type data set for the environmental data collected at each time point; takes the collection time point as a matrix data label, and coordinates the environment type data sets collected at each time point in the period in time order to construct a periodic wheat growth environment data matrix;

[0045] The wheat diseased ear rate analysis unit retrieves the growth environment data matrix of wheat in the current observation area, extracts the environmental data corresponding to each environmental type in the environmental data set at each time point in the matrix, and performs a periodic comprehensive analysis on each type of environmental data; based on the periodic equilibrium value of each type of environmental data in the period, analyzes the fusarium head rate of wheat observed in the period; retrieves the historical environmental data of wheat fusarium head blight, and retrieves the fusarium head rate data of wheat in the corresponding period of the same historical environmental data by referring to the periodic equilibrium value of each type of environmental data corresponding to the current period; and performs an error analysis between the retrieved historical fusarium head rate data and the fusarium head rate of the current period.

[0046] The regional anomaly index analysis module includes an image anomaly point screening unit and a regional anomaly index analysis unit;

[0047] The image abnormal point screening unit retrieves the growth image data of regional wheat in the cycle, and obtains the chromaticity data of each pixel in the image by magnifying the local window of the image; the chromaticity data is the RGB three-channel intensity value corresponding to the pixel; the coordinates of each pixel in the image are obtained by establishing a coordinate system, and the chromaticity data of the corresponding pixel is associated with the coordinate data of the corresponding pixel by constructing a data association channel; the corresponding chromaticity feature vector is constructed based on the chromaticity data corresponding to each pixel in the coordinate system, and the feature vector of each pixel is combined with the standard chromaticity feature vector of historical healthy wheat for screening and analysis; each pixel is judged to be abnormal by setting health parameters; the abnormal pixels are explicitly marked, and the normal pixels are screened out;

[0048] The regional abnormality index analysis unit divides the wheat region coordinate image data into equal regions based on the pixel point screening process, and assigns labels to each sub-region; analyzes the similarity between the abnormal feature points in each sub-region and the chromaticity feature vectors of the standard lesion abnormal pixel points of the wheat with historical ergot; analyzes the ergot abnormality index of the wheat in the sub-region based on the similarity analysis data of the chromaticity feature vectors corresponding to the abnormal pixel points in each sub-region with the standard chromaticity feature vectors of the historical healthy wheat and the chromaticity feature vectors of the standard lesion abnormal pixel points of the wheat with historical ergot; judges whether the wheat in each sub-region has the risk of ergot by introducing a comparison index; if there are multiple sub-regions with the risk of ergot, the monitoring priority is allocated from large to small according to the ergot abnormality index corresponding to each sub-region.

[0049] The prediction output module includes a data visualization output unit and a warning monitoring feedback unit;

[0050] The data visualization output unit visualizes and outputs the diseased ear rate analysis data of the currently observed wheat growth area, the regional image pixel point analysis data, and the regional division data;

[0051] The warning monitoring feedback unit provides warning feedback to sub-areas where there is a risk of ergot disease occurring, and monitors each sub-area according to its priority.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The present invention acquires the fusarium head rate of wheat during its growth cycle by combining wheat growth environment data and image data collection, and performs corresponding environmental data processing, and performs impact ratio analysis in combination with historical data; performs abnormal point screening, division, analysis and processing on the collection, and analyzes the fusarium head abnormality index of the sub-region in combination with the diseased ear rate impact ratio analysis; and performs accurate fusarium head occurrence prediction or positioning prediction on regional wheat in combination with the analysis; the present invention greatly improves the prediction accuracy of wheat fusarium head occurrence, and can accurately locate a large range of wheat fusarium head occurrence areas, improving the current situation of traditional fusarium head prediction being delayed and inaccurate in range. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 The present invention is a structural schematic diagram of a digital management system for a wheat fusarium head blight prediction model. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Example: Figure 1 As shown, the present invention provides a technical solution:

[0057] A digital management method for a wheat scab prediction model, the method comprising the following steps:

[0058] S100, collecting meteorological environment data of wheat growth in the observation area period by using meteorological collection equipment, and simultaneously collecting wheat images in the observation area period by using image collection equipment;

[0059] S200, based on the collected environmental data, construct a wheat growth environment data matrix in the corresponding period with the corresponding collection time point as the data label; analyze the diseased ear rate of wheat environmental data in the current period by combining historical wheat disease environment data;

[0060] S300, performing abnormal pixel location processing by combining the collected image with the pixel chromaticity information of the wheat historical image; dividing the wheat area based on the abnormal pixel points of the current wheat image, and performing pathological feature analysis on the divided sub-areas to predict the pathological sub-areas;

[0061] S400: Visually output the diseased ear rate analysis and regional division information of the current wheat growing area, and issue a warning for the predicted diseased area.

[0062] The specific steps of S100 for collecting meteorological environmental data of wheat growth in the observation area period by using meteorological collection equipment and synchronously collecting wheat images in the observation area period by using image collection equipment are as follows:

[0063] S101, collecting real-time growth environment data of wheat through meteorological equipment of the wheat growth area device; obtaining the growth environment data of wheat in the corresponding period by setting a collection period; and transmitting the collected data to the analysis end by using regional networking;

[0064] S102, collecting images of the entire wheat growing area through an image collection device, and remotely transmitting the collected image data to an analysis terminal through an airborne communication device.

[0065] The S200 constructs a wheat growth environment data matrix in a corresponding period based on the collected environmental data with the corresponding collection time point as the data label; the specific steps of analyzing the diseased ear rate of wheat environmental data in the current period by combining the historical wheat disease environment data are as follows:

[0066] S201, retrieve the wheat growth environment data collected in the period, and classify the collected environment data types by determining the collection time point; construct a corresponding environment type data set for the environment data collected at each time point; use the collection time point as a matrix data label, coordinate the environment type data sets collected at each time point in the period in chronological order, and construct a period wheat growth environment data matrix;

[0067] S202, retrieve the wheat growth environment data matrix of the current observation area, extract the environmental data corresponding to each environmental type in the environmental data set at each time point in the matrix, and perform periodic comprehensive analysis on each type of environmental data. The calculation formula is:

[0068]

[0069] Wherein, PVE(n:T) is the period equilibrium value of the environment data with type number n in the corresponding period T; M(t∈T) is the number of time points t in the period T; t∈T is the time point t in the period T; H(n:t) is the value of the environment data with type number n at each time point t in the corresponding period T;

[0070] Based on the periodic equilibrium value of various types of environmental data within the period, the fusarium head blight rate of wheat observed within the period was analyzed, and the calculation formula is:

[0071]

[0072] Wherein, YDP(T) is the fusarium head rate of wheat observed in period T; Fn is the fusarium head rate influence coefficient of the environmental data corresponding to type number n; by retrieving the historical environmental data of wheat fusarium head rate, and taking the period equilibrium value of the corresponding types of environmental data of the current period as a reference, the fusarium head rate data of wheat in the corresponding period with the same historical environmental data are retrieved; by performing error analysis on the retrieved historical fusarium head rate data and the fusarium head rate of the current period, if |YDP(T)-YDP(AL)|>E, the current observed wheat environmental data are recollected and the above environmental data analysis steps are performed again; wherein YDP(AL) is the mean value of the historical fusarium head rate under the same environmental data of wheat in the current period; E is the error control parameter;

[0073] If |YDP(T)-YDP(AL)|≤E, the comprehensive impact ratio of the diseased ear rate of the currently observed wheat is analyzed, and the calculation formula is:

[0074]

[0075] Among them, POI(T) is the comprehensive impact ratio of the diseased ear rate of wheat observed in the current cycle; YDP(Amax) and YDP(Amin) are the maximum and minimum values ​​of the historical fusarium head blight rate data under the same environmental data of wheat in the current cycle, respectively.

[0076] The S300 performs abnormal pixel location processing by combining the collected image with the pixel color information of the wheat historical image; divides the wheat area based on the abnormal pixel points of the current wheat image, and performs pathological feature analysis based on the divided sub-areas. The specific steps of predicting the pathological sub-area are as follows:

[0077] S301, retrieve the growth image data of wheat in the region, and obtain the chromaticity data of each pixel in the image by magnifying the local window of the image; the chromaticity data is the RGB three-channel intensity value corresponding to the pixel; obtain the coordinates of each pixel in the image by establishing a coordinate system, and associate the chromaticity data of the corresponding pixel with the coordinate data of the corresponding pixel by constructing a data association channel; construct the corresponding chromaticity feature vector based on the chromaticity data corresponding to each pixel in the coordinate system, and screen and analyze the feature vector of each pixel with the standard chromaticity feature vector of historical healthy wheat, and the calculation formula is:

[0078]

[0079] Among them, D[(x,y):HE] refers to the similarity between the chromaticity feature vector of the pixel point with coordinates (x,y) and the standard chromaticity feature vector of historical healthy wheat; is the chromaticity feature vector of the pixel corresponding to the coordinate (x, y); is the standard chromaticity feature vector of historical healthy wheat; and The chromaticity feature vector of the corresponding pixel with coordinates (x, y) is the modulus of the standard chromaticity feature vector of historical healthy wheat; the chromaticity feature vector of the corresponding coordinates is Among them I R ,I G ,I B The chromaticity intensity values ​​of the R, G, and B channels corresponding to the coordinate pixel points respectively; by setting the health parameter Dp, if D[(x, y):HE]≤Dp, the pixel point corresponding to the current coordinate is judged to be a normal pixel point; if D[(x, y):HE]>Dp, the pixel point corresponding to the current coordinate is judged to be an abnormal pixel point; the abnormal pixel points are explicitly marked, and the normal pixel points are screened out;

[0080] S302, based on the wheat area coordinate image data after pixel point screening, equal area division is performed, and labels are assigned to each sub-area; by analyzing the similarity of the chromaticity feature vectors of the abnormal feature points in each sub-area with the standard lesion abnormal pixel points of the historical fusarium rust wheat, the calculation formula is:

[0081]

[0082] Among them, D[(x,y) j :SC] refers to the similarity between the chromaticity feature vector of the pixel point with coordinate (x, y) in the corresponding sub-region number j and the chromaticity feature vector of the standard abnormal lesion pixel point of the historical scab disease wheat; is the chromaticity feature vector of the pixel with coordinate (x, y) in the corresponding sub-region number j, is the chromaticity feature vector of the standard abnormal lesion pixel of historical fusarium rust wheat, is the modulus of the chromaticity feature vector of the pixel with coordinate (x, y) in the corresponding sub-region number j, is the modulus of the chromaticity feature vector of the standard abnormal lesion pixel of the historical fusarium head blight wheat;

[0083] Combining the similarity analysis data of the chromaticity feature vectors corresponding to the abnormal pixels in each sub-region with the standard chromaticity feature vectors of historical healthy wheat and the chromaticity feature vectors of the standard abnormal pixels of lesions in historical fusarium rust wheat, the fusarium rust abnormality index of wheat in the sub-region was analyzed, and the calculation formula was:

[0084]

[0085] Among them, GI j is the abnormal index of wheat fusarium wilt in the corresponding sub-region numbered j; f is the activation function; U j is the number of outliers in the sub-region numbered j; Ua is the number of abnormal points in the whole area of ​​the wheat image; D[(x,y) j :HE] is the similarity between the chromaticity feature vector of the pixel with coordinate (x, y) in the corresponding sub-region number j and the standard chromaticity feature vector of historical healthy wheat; b is the bias parameter; (x, y)∈j is the coordinate of each pixel in the sub-region number j; by introducing the contrast index GI(th), if GI j <GI(th), then it is judged that there is no risk of fusarium fusarium in the current sub-region corresponding to number j; if GI j ≥GI(th), it is judged that the wheat in the current sub-region corresponding to number j is at risk of fusarium ergot; if there are multiple sub-regions with the risk of fusarium ergot, the monitoring priority is allocated from large to small according to the corresponding fusarium ergot abnormality index of each sub-region.

[0086] The specific steps of S400 for visually outputting the diseased ear rate analysis and regional division information of the current wheat growing area and warning the predicted diseased area are as follows:

[0087] S401, visually output the diseased ear rate analysis data of the currently observed wheat growth area, the regional image pixel point analysis data, and the regional division data;

[0088] S402. Provide warning feedback to sub-areas with risk of ergot disease and conduct monitoring based on the priority of each sub-area.

[0089] A digital management system for a wheat scab prediction model, the system comprising a regional data acquisition module, a periodic diseased ear rate analysis module, a regional abnormal index analysis module and a prediction output module;

[0090] The regional data acquisition module collects meteorological environment data of wheat growth within the observation area period through meteorological acquisition equipment, and simultaneously uses image acquisition equipment to collect wheat images within the observation area period; the periodic diseased ear rate analysis module constructs a wheat growth environment data matrix within the corresponding period based on the collected environment data, with the corresponding collection time point as the data label; the diseased ear rate of wheat environment data in the current period is analyzed by combining historical wheat pathological environment data; the regional abnormal index analysis module performs abnormal pixel location processing by combining the collected image with the chromaticity information of the pixel points of the historical wheat image; the wheat area is divided according to the abnormal pixels of the current wheat image, and the pathological characteristics are analyzed in combination with the divided sub-areas, and the pathological sub-areas are predicted; the prediction output module visualizes the diseased ear rate analysis and regional division information of the current wheat growth area, and warns the predicted pathological area.

[0091] The regional data acquisition module includes a regional environment data acquisition unit and a wheat image data acquisition unit;

[0092] The regional environmental data acquisition unit collects real-time wheat growth environment data through the meteorological equipment of the wheat growth area device; obtains the wheat growth environment data within the corresponding period by setting the acquisition cycle; and transmits the collected data to the analysis end by using the regional networking;

[0093] The wheat image data acquisition unit acquires images of the entire wheat growing area through an image acquisition device, and remotely transmits the acquired image data to an analysis end through an airborne communication device.

[0094] The periodic diseased ear rate analysis module includes an environmental data processing unit and a wheat diseased ear rate analysis unit;

[0095] The environmental data processing unit retrieves the wheat growth environment data collected periodically, and classifies the collected environment data according to the corresponding collected environment data type by determining the collection time point; constructs a corresponding environment type data set for the environmental data collected at each time point; takes the collection time point as a matrix data label, and coordinates the environment type data sets collected at each time point in the period in time order to construct a periodic wheat growth environment data matrix;

[0096] The wheat diseased ear rate analysis unit retrieves the growth environment data matrix of wheat in the current observation area, extracts the environmental data corresponding to each environmental type in the environmental data set at each time point in the matrix, and performs a periodic comprehensive analysis on each type of environmental data; based on the periodic equilibrium value of each type of environmental data in the period, analyzes the fusarium head rate of wheat observed in the period; retrieves the historical environmental data of wheat fusarium head blight, and retrieves the fusarium head rate data of wheat in the corresponding period of the same historical environmental data by referring to the periodic equilibrium value of each type of environmental data corresponding to the current period; and performs an error analysis between the retrieved historical fusarium head rate data and the fusarium head rate of the current period.

[0097] The regional anomaly index analysis module includes an image anomaly point screening unit and a regional anomaly index analysis unit;

[0098] The image abnormal point screening unit retrieves the growth image data of regional wheat in the cycle, and obtains the chromaticity data of each pixel in the image by magnifying the local window of the image; the chromaticity data is the RGB three-channel intensity value corresponding to the pixel; the coordinates of each pixel in the image are obtained by establishing a coordinate system, and the chromaticity data of the corresponding pixel is associated with the coordinate data of the corresponding pixel by constructing a data association channel; the corresponding chromaticity feature vector is constructed based on the chromaticity data corresponding to each pixel in the coordinate system, and the feature vector of each pixel is combined with the standard chromaticity feature vector of historical healthy wheat for screening and analysis; each pixel is judged to be abnormal by setting health parameters; the abnormal pixels are explicitly marked, and the normal pixels are screened out;

[0099] The regional abnormality index analysis unit divides the wheat region coordinate image data into equal regions based on the pixel point screening process, and assigns labels to each sub-region; analyzes the similarity between the abnormal feature points in each sub-region and the chromaticity feature vectors of the standard lesion abnormal pixel points of the wheat with historical ergot; analyzes the ergot abnormality index of the wheat in the sub-region based on the similarity analysis data of the chromaticity feature vectors corresponding to the abnormal pixel points in each sub-region with the standard chromaticity feature vectors of the historical healthy wheat and the chromaticity feature vectors of the standard lesion abnormal pixel points of the wheat with historical ergot; judges whether the wheat in each sub-region has the risk of ergot by introducing a comparison index; if there are multiple sub-regions with the risk of ergot, the monitoring priority is allocated from large to small according to the ergot abnormality index corresponding to each sub-region.

[0100] The prediction output module includes a data visualization output unit and a warning monitoring feedback unit;

[0101] The data visualization output unit visualizes and outputs the diseased ear rate analysis data of the currently observed wheat growth area, the regional image pixel point analysis data, and the regional division data;

[0102] The warning monitoring feedback unit provides warning feedback to sub-areas with the risk of fusarium smut, and monitors each sub-area according to its priority;

[0103] In the example:

[0104] A certain wheat planting base needs to monitor the wheat growing area for fusarium head blight, and is equipped with the digital management system for the wheat fusarium head blight prediction model of the present invention; the base collects the real-time growth environment data of wheat through the meteorological equipment of the wheat growing area device; obtains the growth environment data of wheat in the corresponding period by setting the collection cycle; transmits the collected data to the analysis end by using the regional networking; collects the whole area image of the wheat growing area by the image collection device, and transmits the collected image data to the analysis end by remote data transmission through the airborne communication equipment;

[0105] Retrieve the wheat growth environment data collected periodically, and classify the collected environment data types by determining the collection time point; construct the corresponding environment type data set for the environmental data collected at each time point; use the collection time point as the matrix data label, coordinate the environment type data sets collected at each time point in the period in chronological order, and construct a periodic wheat growth environment data matrix; retrieve the wheat growth environment data matrix in the current observation area, extract the environmental data corresponding to each environment type in the environmental data set at each time point in the matrix, and conduct a periodic comprehensive analysis of each type of environmental data. The calculation formula is:

[0106]

[0107] Based on the periodic equilibrium value of various types of environmental data within the period, the fusarium head blight rate of wheat observed within the period was analyzed, and the calculation formula is:

[0108]

[0109] By retrieving the historical environmental data of wheat fusarium head blight, and taking the period equilibrium value of each type of environmental data corresponding to the current period as a reference, the fusarium head blight rate data of wheat in the corresponding period of the same historical environmental data are retrieved; by performing error analysis on the retrieved historical fusarium head blight rate data and the fusarium head blight rate of the current period, if |YDP(T)-YDP(AL)|>E, the currently observed wheat environmental data are recollected and the above environmental data analysis steps are performed again;

[0110] If |YDP(T)-YDP(AL)|≤E, the comprehensive impact ratio of the diseased ear rate of the currently observed wheat is analyzed, and the calculation formula is:

[0111]

[0112] The growth image data of regional wheat in the cycle is retrieved, and the chromaticity data of each pixel in the image is obtained by magnifying the local window of the image; the chromaticity data is the RGB three-channel intensity value corresponding to the pixel; the coordinates of each pixel in the image are obtained by establishing a coordinate system, and the chromaticity data of the corresponding pixel is associated with the coordinate data of the corresponding pixel by constructing a data association channel; the corresponding chromaticity feature vector is constructed based on the chromaticity data corresponding to each pixel in the coordinate system, and the feature vector of each pixel is combined with the standard chromaticity feature vector of historical healthy wheat for screening and analysis, and the calculation formula is:

[0113]

[0114] The chromaticity eigenvector of the corresponding coordinates is By setting the health parameter Dp, if D[(x,y):HE]≤Dp, the pixel corresponding to the current coordinate is judged to be a normal pixel; if D[(x,y):HE]>Dp, the pixel corresponding to the current coordinate is judged to be an abnormal pixel; the abnormal pixel is explicitly marked and the normal pixel is screened out;

[0115] Based on the coordinate image data of the wheat area after pixel point screening, the area is divided into equal areas, and labels are assigned to each sub-area. The similarity between the chromaticity feature vectors of the abnormal feature points in each sub-area and the standard abnormal pixel points of the historical fusarium rust wheat is analyzed, and the calculation formula is:

[0116]

[0117] Combining the similarity analysis data of the chromaticity feature vectors corresponding to the abnormal pixels in each sub-region with the standard chromaticity feature vectors of historical healthy wheat and the chromaticity feature vectors of the standard abnormal pixels of lesions in historical fusarium rust wheat, the fusarium rust abnormality index of wheat in the sub-region was analyzed, and the calculation formula was:

[0118]

[0119] By introducing the contrast index GI(th), if GI j <GI(th), then it is judged that there is no risk of fusarium fusarium in the current sub-region corresponding to number j; if GI j ≥GI(th), it is judged that the wheat in the current sub-region corresponding to number j is at risk of fusarium wilt; if there are multiple sub-regions with fusarium wilt risk, the monitoring priority is allocated from large to small according to the corresponding fusarium wilt abnormality index of each sub-region;

[0120] The diseased ear rate analysis data of the currently observed wheat growing area, the regional image pixel analysis data and the regional division data are visualized and output; warning feedback is given to sub-areas with the risk of ergot disease, and monitoring is carried out according to the priority of each sub-area.

[0121] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A digital management method for a wheat scab prediction model, characterized in that: The method comprises the following steps: S100, collecting meteorological environment data of wheat growth in the observation area period by using meteorological collection equipment, and simultaneously collecting wheat images in the observation area period by using image collection equipment; S200, based on the collected environmental data, construct a wheat growth environment data matrix in the corresponding period with the corresponding collection time point as the data label; analyze the diseased ear rate of wheat environmental data in the current period by combining historical wheat disease environment data; S300, performing abnormal pixel location processing by combining the collected image with the pixel chromaticity information of the wheat historical image; dividing the wheat area based on the abnormal pixel points of the current wheat image, and performing pathological feature analysis based on the divided sub-areas to predict the pathological sub-areas; S400: Visually output the diseased ear rate analysis and regional division information of the current wheat growing area, and issue a warning for the predicted diseased area.

2. A digital management method for wheat scab prediction model according to claim 1, characterized in that: The specific steps of S100 for collecting meteorological environmental data of wheat growth in the observation area period by using meteorological collection equipment and synchronously collecting wheat images in the observation area period by using image collection equipment are as follows: S101, collecting real-time growth environment data of wheat through meteorological equipment of the wheat growth area device; obtaining the growth environment data of wheat in the corresponding period by setting a collection period; and transmitting the collected data to the analysis end by using regional networking; S102, collecting images of the entire wheat growing area through an image collection device, and remotely transmitting the collected image data to an analysis terminal through an airborne communication device.

3. A digital management method for wheat scab prediction model according to claim 2, characterized in that: The S200 constructs a wheat growth environment data matrix in a corresponding period based on the collected environmental data with the corresponding collection time point as the data label; the specific steps of analyzing the diseased ear rate of wheat environmental data in the current period by combining the historical wheat disease environment data are as follows: S201, retrieve the wheat growth environment data collected periodically, and classify the data according to the corresponding collected environment data type by determining the collection time point; and construct a corresponding environment type data set for the environment data collected at each time point; Taking the collection time point as the matrix data label, the environmental type data sets collected at each time point in the cycle are coordinated in chronological order to construct the periodic wheat growth environment data matrix; S202, retrieve the wheat growth environment data matrix of the current observation area, extract the environmental data corresponding to each environmental type in the environmental data set at each time point in the matrix, and perform periodic comprehensive analysis on each type of environmental data. The calculation formula is: Wherein, PVE(n:T) is the period equilibrium value of the environment data with type number n in the corresponding period T; M(t∈T) is the number of time points t in the period T; t∈T is the time point t in the period T; H(n:t) is the value of the environment data with type number n at each time point t in the corresponding period T; Based on the periodic equilibrium value of various types of environmental data within the period, the fusarium head blight rate of wheat observed within the period was analyzed, and the calculation formula is: Wherein, YDP(T) is the fusarium head rate of wheat observed in period T; Fn is the fusarium head rate influence coefficient of the environmental data corresponding to type number n; by retrieving the historical environmental data of wheat fusarium head rate, and taking the period equilibrium value of the corresponding types of environmental data of the current period as a reference, the fusarium head rate data of wheat in the corresponding period with the same historical environmental data are retrieved; by performing error analysis on the retrieved historical fusarium head rate data and the fusarium head rate of the current period, if |YDP(T)-YDP(AL)|>E, the current observed wheat environmental data are recollected and the above environmental data analysis steps are performed again; wherein YDP(AL) is the mean value of the historical fusarium head rate under the same environmental data of wheat in the current period; E is the error control parameter; If |YDP(T)-YDP(AL)|≤E, the comprehensive impact ratio of the diseased ear rate of the currently observed wheat is analyzed, and the calculation formula is: Among them, POI(T) is the comprehensive impact ratio of the diseased ear rate of wheat observed in the current cycle; YDP(Amax) and YDP(Amin) are the maximum and minimum values ​​of the historical fusarium head blight rate data under the same environmental data of wheat in the current cycle, respectively.

4. A digital management method for wheat scab prediction model according to claim 3, characterized in that: The S300 performs abnormal pixel location processing by combining the collected image with the pixel color information of the wheat historical image; divides the wheat area based on the abnormal pixel points of the current wheat image, and performs pathological feature analysis based on the divided sub-areas. The specific steps of predicting the pathological sub-area are as follows: S301, retrieve the growth image data of wheat in the region, and obtain the chromaticity data of each pixel in the image by magnifying the local window of the image; the chromaticity data is the RGB three-channel intensity value corresponding to the pixel; obtain the coordinates of each pixel in the image by establishing a coordinate system, and associate the chromaticity data of the corresponding pixel with the coordinate data of the corresponding pixel by constructing a data association channel; construct the corresponding chromaticity feature vector based on the chromaticity data corresponding to each pixel in the coordinate system, and screen and analyze the feature vector of each pixel with the standard chromaticity feature vector of historical healthy wheat, and the calculation formula is: Among them, D[(x,y):HE] refers to the similarity between the chromaticity feature vector of the pixel point with coordinates (x,y) and the standard chromaticity feature vector of historical healthy wheat; is the chromaticity feature vector of the pixel corresponding to the coordinate (x, y); is the standard chromaticity feature vector of historical healthy wheat; and The chromaticity feature vector of the pixel corresponding to the coordinate (x, y) is the modulus of the standard chromaticity feature vector of historical healthy wheat; the chromaticity feature vector of the corresponding coordinate is Among them I R ,I G ,I B The chromaticity intensity values ​​of the R, G, and B channels corresponding to the coordinate pixel points respectively; by setting the health parameter Dp, if D[(x, y):HE]≤Dp, the pixel point corresponding to the current coordinate is judged to be a normal pixel point; if D[(x, y):HE]>Dp, the pixel point corresponding to the current coordinate is judged to be an abnormal pixel point; the abnormal pixel points are explicitly marked, and the normal pixel points are screened out; S302, based on the wheat area coordinate image data after pixel point screening, equal area division is performed, and labels are assigned to each sub-area; by analyzing the similarity of the chromaticity feature vectors of the abnormal feature points in each sub-area with the standard lesion abnormal pixel points of the historical fusarium rust wheat, the calculation formula is: Among them, D[(x,y) j :SC] refers to the similarity between the chromaticity feature vector of the pixel point with coordinate (x, y) in the corresponding sub-region number j and the chromaticity feature vector of the standard abnormal lesion pixel point of the historical scab disease wheat; is the chromaticity feature vector of the pixel with coordinates (x, y) in the corresponding sub-region number j, is the chromaticity feature vector of the standard abnormal lesion pixel of historical fusarium scab wheat, is the modulus of the chromaticity feature vector of the pixel with coordinate (x, y) in the corresponding sub-region number j, is the modulus of the chromaticity feature vector of the standard abnormal lesion pixel of the historical fusarium head blight wheat; Combining the similarity analysis data of the chromaticity feature vectors corresponding to the abnormal pixels in each sub-region with the standard chromaticity feature vectors of historical healthy wheat and the chromaticity feature vectors of the standard abnormal pixels of lesions in historical fusarium scaly wheat, the fusarium scaly abnormal index of wheat in the sub-region was analyzed, and its calculation formula is: Among them, GI j is the abnormal index of wheat fusarium wilt in the corresponding sub-region numbered j; f is the activation function; U j is the number of outliers in the sub-region numbered j; U a is the number of abnormal points in the whole area of ​​the wheat image; D[(x,y) j :HE] is the similarity between the chromaticity feature vector of the pixel with coordinate (x, y) in the corresponding sub-region number j and the standard chromaticity feature vector of historical healthy wheat; b is the bias parameter; (x, y)∈j is the coordinate of each pixel in the sub-region number j; by introducing the contrast index GI(th), if GI j <GI(th), then it is judged that there is no risk of fusarium fusarium in the current sub-region corresponding to number j; if GI j ≥GI(th), it is judged that the wheat in the current sub-region corresponding to number j is at risk of fusarium ergot; if there are multiple sub-regions with the risk of fusarium ergot, the monitoring priority is allocated from large to small according to the corresponding fusarium ergot abnormality index of each sub-region.

5. A digital management method for wheat scab prediction model according to claim 4, characterized in that: The specific steps of S400 for visually outputting the diseased ear rate analysis and regional division information of the current wheat growing area and warning the predicted diseased area are as follows: S401, visually output the diseased ear rate analysis data of the currently observed wheat growth area, the regional image pixel point analysis data, and the regional division data; S402. Provide warning feedback to sub-areas with risk of ergot disease and conduct monitoring based on the priority of each sub-area.

6. A digital management system for wheat scab prediction model, characterized in that: The system includes a regional data acquisition module, a periodic diseased ear rate analysis module, a regional abnormal index analysis module and a prediction output module; The regional data acquisition module collects meteorological environment data of wheat growth within the observation area period through meteorological acquisition equipment, and simultaneously uses image acquisition equipment to collect wheat images within the observation area period; the periodic diseased ear rate analysis module constructs a wheat growth environment data matrix within the corresponding period based on the collected environment data, with the corresponding collection time point as the data label; the diseased ear rate of wheat environment data in the current period is analyzed by combining historical wheat pathological environment data; the regional abnormal index analysis module performs abnormal pixel location processing by combining the collected image with the chromaticity information of the pixel points of the historical wheat image; the wheat area is divided according to the abnormal pixels of the current wheat image, and the pathological characteristics are analyzed in combination with the divided sub-areas, and the pathological sub-areas are predicted; the prediction output module visualizes the diseased ear rate analysis and regional division information of the current wheat growth area, and warns the predicted pathological area.

7. A digital management system for wheat scab prediction model according to claim 6, characterized in that: The regional data acquisition module includes a regional environment data acquisition unit and a wheat image data acquisition unit; The regional environmental data acquisition unit collects real-time wheat growth environment data through the meteorological equipment of the wheat growth area device; obtains the wheat growth environment data within the corresponding period by setting the acquisition cycle; and transmits the collected data to the analysis end by using the regional networking; The wheat image data acquisition unit acquires images of the entire wheat growing area through an image acquisition device, and remotely transmits the acquired image data to an analysis end through an airborne communication device.

8. A digital management system for wheat scab prediction model according to claim 7, characterized in that: The periodic diseased ear rate analysis module includes an environmental data processing unit and a wheat diseased ear rate analysis unit; The environmental data processing unit retrieves the periodically collected wheat growth environmental data, and classifies and processes the data according to the collected environmental data type by determining the collection time point; Construct corresponding environmental type data sets for environmental data collected at each time point; Taking the collection time point as the matrix data label, the environmental type data sets collected at each time point in the cycle are coordinated in chronological order to construct the periodic wheat growth environment data matrix; The wheat diseased ear rate analysis unit retrieves the growth environment data matrix of wheat in the current observation area, extracts the environmental data corresponding to each environmental type in the environmental data set at each time point in the matrix, and performs a periodic comprehensive analysis on each type of environmental data; based on the periodic equilibrium value of each type of environmental data in the period, analyzes the fusarium head rate of wheat observed in the period; retrieves the historical environmental data of wheat fusarium head blight, and retrieves the fusarium head rate data of wheat in the corresponding period of the same historical environmental data by referring to the periodic equilibrium value of each type of environmental data corresponding to the current period; and performs an error analysis between the retrieved historical fusarium head rate data and the fusarium head rate of the current period.

9. A digital management system for wheat scab prediction model according to claim 8, characterized in that: The regional anomaly index analysis module includes an image anomaly point screening unit and a regional anomaly index analysis unit; The image abnormal point screening unit retrieves the growth image data of wheat in the region within the cycle, and obtains the chromaticity data of each pixel in the image by magnifying the local window of the image; The chromaticity data is the RGB three-channel intensity value corresponding to the pixel point; the coordinates of each pixel point in the image are obtained by establishing a coordinate system, and the chromaticity data of the corresponding pixel point is associated with the coordinate data of the corresponding pixel point by constructing a data association channel; the corresponding chromaticity feature vector is constructed based on the chromaticity data corresponding to each pixel point in the coordinate system, and the feature vector of each pixel point is combined with the standard chromaticity feature vector of historical healthy wheat for screening and analysis; each pixel point is judged to be abnormal by setting health parameters; the abnormal pixel points are explicitly marked, and the normal pixel points are screened out; The regional abnormality index analysis unit divides the wheat region coordinate image data into equal regions based on the pixel point screening process, and assigns labels to each sub-region; analyzes the similarity between the abnormal feature points in each sub-region and the chromaticity feature vectors of the standard abnormal lesion pixel points of the wheat with historical fusarium rust; combines the similarity analysis data of the chromaticity feature vectors corresponding to the abnormal pixels in each sub-region with the standard chromaticity feature vectors of the healthy wheat in history and the chromaticity feature vectors of the standard abnormal lesion pixel points of the wheat with historical fusarium rust, and analyzes the abnormal fusarium rust index of the wheat in the sub-region; and introduces a comparison index to determine whether the wheat in each sub-region has a risk of fusarium rust; If there are multiple sub-regions with the risk of ergot disease, monitoring priorities will be allocated from large to small based on the corresponding ergot disease abnormality index of each sub-region.

10. A digital management system for wheat scab prediction model according to claim 9, characterized in that: The prediction output module includes a data visualization output unit and a warning monitoring feedback unit; The data visualization output unit visualizes and outputs the diseased ear rate analysis data of the currently observed wheat growth area, the regional image pixel point analysis data, and the regional division data; The warning monitoring feedback unit provides warning feedback to sub-areas where there is a risk of ergot disease occurring, and monitors each sub-area according to its priority.

Images