CN116448760A - Agricultural intelligent monitoring system and method based on machine vision - Google Patents
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Abstract
The invention discloses an agricultural intelligent monitoring system and method based on machine vision, and belongs to the field of agricultural intelligent monitoring. According to the invention, basic data information is collected, the surface image of the crops is collected through machine vision, the reason that the crops are subjected to diseases and insect pests is analyzed, the distribution trend of the diseases and insect pests is predicted, a user is reminded and suggestions for preventing and controlling the diseases and insect pests are given, the influence of the diseases and insect pests on the crops is reduced, the yield of the crops is improved, and the efficiency of agricultural monitoring is improved.
Description
Technical Field
The invention relates to the field of agricultural intelligent monitoring, in particular to an agricultural intelligent monitoring system and method based on machine vision.
Background
With the forward evolution of society, the scientific technology is rapidly developed, the mechanical field is also developed as a field with more application to the scientific technology, and accordingly, the mechanical automation is more and more widely penetrated into the aspects of life of people. Mechanical automation brings great convenience for daily life, and improves the efficiency of life activities. Agriculture is an important industrial department in national economy as the first industry of the country, supports the construction and development of the national economy, and has the development trend of high knowledge, socialization, internationalization, commercialization, capital, large-scale, specialization, regionalization, industrialization and other positive factors which are interwoven and fused together.
Crop diseases and insect pests are one of main agricultural disasters in China, and have the characteristics of multiple types, large influence and frequent outbreaks of disasters, and the occurrence range and the severity of the crop diseases and insect pests often cause great losses to national economy of China, particularly agricultural production. In the prior art, pest and disease damage blades of crops are identified, the blades affected by the pests are identified, pesticide spraying is performed aiming at the identified reasons, however, if pesticide spraying is performed in a small range, the pest and disease damage control speed is slow, and if pesticide spraying is performed in a large range, pollution is generated, and even the pesticide damage is generated to the crops.
It is quite necessary to provide a control suggestion according to the spreading trend of the plant diseases and insect pests of the crops. Therefore, there is a need for an agricultural intelligent monitoring system and method based on machine vision.
Disclosure of Invention
The invention aims to provide an agricultural intelligent monitoring system and method based on machine vision, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an agricultural intelligent monitoring method based on machine vision comprises the following steps:
s1, acquiring basic data information, acquiring crop surface image information through a camera, acquiring position information of the camera through a GPS, and performing encryption storage;
s2, analyzing and processing the surface image of the crop according to the acquired basic data information and image information, predicting the reason of plant diseases and insect pests of the crop, and providing advice;
s3, analyzing and processing the change trend of the crop subjected to the plant diseases and insect pests according to the analyzed crop surface image information;
and S4, displaying the user through a display device according to the analysis result, and reminding the user to take preventive and therapeutic measures as soon as possible through a voice device.
Further, in step S2, the following steps are included:
s201, performing binarization pretreatment on the collected crop surface image according to the collected crop surface image information and the position information of the camera equipment, and marking and dividing an agricultural area;
s202, extracting the outline of the surface of the crop in the image, comparing the outline with basic pest information stored in a database, and identifying the received pest situation to obtain a pest area distribution image, wherein the method comprises the following steps:
s202-1, calculating a two-dimensional distribution function f (x, y) through the following formula:
wherein (x, y) represents the pixel coordinates in the mask, (x) 0 ,y 0 ) Representing coordinates of a center point of a mask, wherein the mask of the image is used for shielding a processed image (whole or partial) by using a selected image, a graph or an object to control an area or a processing process of image processing, and Gaussian filtering is carried out on the image through the mask; sigma represents standard deviation; calculating coefficient values of each point in the mask through a two-dimensional distribution function to obtain convolution factors in eight directions;
s202-2, performing convolution operation on the preprocessed image and eight direction templates to obtain eight-direction contour information S 0° ，S 45° ，S 90° ，S 135° ，S 180° ，S 225° ，S 270° ，S 315° The point (x i ，y i ) Gray value H (x i ，y i ) And (3) performing calculation:
S 1 ＝S 0 ° 2 +S 45 ° 2 +S 90 ° 2 +S 135 ° 2 ；
S 2 ＝S 180 ° 2 +S 225 ° 2 +S 270 ° 2 +S 315 ° 2 ；
s202-3, setting the gray threshold value as H Threshold value The image contour gray value L (x, y) is calculated by the following formula:
when the gray value of the image contour is larger than or equal to 255, judging the image contour; otherwise, determining that the contour is non-contour;
s202-4, processing the extracted image contour through a region growing algorithm, wherein region growing is a serial region segmentation image segmentation method, region growing refers to gradually adding adjacent pixels from a certain pixel according to a certain criterion, and when certain conditions are met, the region growing is stopped, the whole region is obtained, and the extraction of a target is realized;
the Jacquard distance s between the acquired profile image A and the basic pest image information B in the database is calculated by the following formula:
wherein α is denoted as a hyper-parameter; setting the threshold value as s Threshold value When s is>s Threshold value When s is less than or equal to s, the similarity of the representation images is low Threshold value When the similarity of the representation images is high; the method comprises the steps of comparing and identifying plant diseases and insect pests to obtain corresponding plant diseases and insect pests, giving control suggestions to users according to basic plant disease and insect pest control information, marking the positions of shooting equipment where plant disease and insect pest images exist according to the collected position information, and combining an agricultural electronic map to obtain a plant disease and insect pest area distribution image;
further, S203, fitting the pest and disease area distribution image to obtain a crop pest and disease distribution situation image of the whole area, including the following steps:
s203-1, smoothing the pest and disease damage area distribution image by the following formula:
Z′(a，b)＝h(a，b)*z(a，b)；
where a and b are represented as pixel values of the image, Z' (a, b) is represented as a smoothed output image, Z (a, b) is represented as an input image, and h (a, b) is represented as a smoothed convolution filter, which may be an average filter or a gaussian smoothing filter; noise interference can be removed by smoothing the image, and extra errors of detail texture edges in the pest and disease area distribution image can be reduced;
edge sharpening is performed on the smoothed image by the following formula:
Z(a，b)＝c|Z′(a，b)-Z′(a+1，b)|；
wherein Z (a, b) represents sharpening the output image, c represents template gain for highlighting the sharpened edge feature, the image sharpening is to compensate the outline of the image, enhance the edge and gray jump part of the image, and make the image clear;
s203-2, matching the processed images, and matching the adjacent image samples Z by the following formula 1 (a, b) and Z 2 The similarity distance D between (a, b) is calculated:
D＝(∑ a＝0 ∑ b＝0 |Z 1 (n-1-N+a，b)-Z 2 (a，b)|)/N；
wherein N represents the number of columns searched in the image sample, and N represents the number of columns searched in the image sample; when D reaches the minimum value, the number N of search columns in the image sample is the same as the size of the overlapping area of the adjacent images, and the images are matched;
s203-3, performing image fitting on the pest and disease damage regional distribution map through the following formula:
P＝(1-β)P 1 +βP 2 ；
wherein P is expressed as an overall pest and disease damage distribution condition image, P 1 and P2 Adjacent image samples, denoted as having overlapping regions, β being denoted as a weighting factor;
s204, predicting the reason of the crop suffering from the plant diseases and insect pests according to the fitted integral plant diseases and insect pests distribution situation image, and suggesting the user.
Further, in step S3, the following steps are included:
s301, building a disease and pest distribution prediction model, and obtaining the fitted integral disease according to timeDividing the pest distribution condition image according to a dividing scale to obtain a pest distribution area, extracting gray values in each wave band to form a state matrix of the area, and marking the state matrix at t-1 day as U t-1 The state matrix on the t-th day is recorded as U t ；
S302, calculating the pest and disease damage change probability P from the t-1 day to the t day by the following formula:
U t ＝PU t-1 ；
wherein ,pγδ Expressed as the probability of the gray value changing from gamma to delta, p γδ Is non-negative, and the sum of each row is 1;
s303, predicting the distribution situation of the plant diseases and insect pests according to the probability prediction model to obtain a predicted plant diseases and insect pests distribution image.
Further, in step S4, the analysis result and the advice are displayed to the user through the screen display device, and the user is reminded to take the prevention and treatment measures as soon as possible through the voice device.
An agricultural intelligent monitoring system based on machine vision, the agricultural intelligent monitoring system comprising: the system comprises a data monitoring module, a database, a data analysis module and an agricultural guidance module;
the output end of the data monitoring module is connected with the input end of the database, the output end of the database is connected with the input end of the data analysis module, the output end of the data analysis module is connected with the input end of the agricultural guidance module, and the output end of the data analysis module is connected with the input end of the database; the data monitoring module is used for collecting basic data information, carrying out image acquisition on plant growth, the database is used for carrying out encryption storage on collected data and analysis results, the data analysis module is used for carrying out analysis processing on the collected plant data, and the agricultural guidance module is used for reminding and guiding a user according to the analysis results.
Further, the data monitoring module comprises a basic data acquisition unit, an image acquisition unit and a position acquisition unit, wherein the basic data acquisition unit is used for inputting agricultural basic data information, providing a comparison basis for image analysis processing, enabling an analysis result to be more accurate, the image acquisition unit performs image acquisition on the surface of crops through image pickup equipment, and the position acquisition unit performs position positioning on the image pickup equipment through a GPS.
Further, the database includes a data encryption unit and a data storage unit, where the data encryption unit encrypts the collected data and the analysis result through an SHA encryption algorithm, so as to ensure the security and privacy of the data, where the SHA encryption algorithm refers to a secure hash algorithm, several one-way hash algorithms including SHA-1, SHA-224, SHA-256, SHA-384 and SHA-512 are specified, the SHA-1, SHA-224 and SHA-256 are applicable to messages with a length of no more than 2 a 64 binary bits, the SHA-384 and SHA-512 are applicable to messages with a length of no more than 2 a 128 binary bits, the hash is an extraction of information, and typically, the length is much smaller than the information, and is a fixed length, and the hash with strong encryption must be irreversible, which means that by the hash result, any part of the original information cannot be deduced, even if any input information changes, only one bit will result in a significant change of the hash result, which is called avalanche effect, and whether the hash should be used to verify whether the hash results have the same characteristics as if two hash results can be modified. The data storage unit stores collected data and analysis results through an HBase column database, wherein the HBase column database is a distributed, extensible and NoSQL database supporting mass data storage, a database supporting high-capacity storage and high-efficiency real-time query, and a database system with high reliability, high performance, column storage, scalability and real-time reading and writing is provided.
Further, the data analysis module comprises an image analysis unit and a change analysis unit, wherein the image analysis unit is used for carrying out data analysis on the collected crop surface images, obtaining target crop images through data fitting, predicting the reasons of diseases and insect pests generated by crops and giving suggestions for controlling the diseases and insect pests, such as the existence of a ditch or the like, and the change analysis unit is used for carrying out analysis processing on the change trend of the crops subjected to the diseases and insect pests according to time change.
Further, the agricultural guidance module comprises a screen display unit and a voice reminding unit, the screen display unit displays analysis results and suggestions to a user through screen display equipment, and the voice reminding unit is used for reminding the user to take prevention and treatment measures as soon as possible through voice equipment, so that the user can take measures to the agricultural pest and disease problems quickly, large-scale damage is avoided, agricultural planting loss is reduced, the yield of agricultural production is improved, and the economic property safety of the user is guaranteed.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, basic data information is collected, a standard is provided for data processing, a crop surface image is collected through machine vision, the crop is analyzed for diseases and insect pests, according to the distribution situation of the diseases and insect pests, not only can the reasons of the diseases and insect pests be known from plants, but also the reasons of the diseases and insect pests can be predicted from environmental factors, and meanwhile, the change trend of the distribution of the diseases and insect pests is predicted according to the overall distribution situation of the diseases and insect pests at different moments, so that a user is reminded and advice for preventing and controlling the diseases and insect pests is provided, for example, the user is advised to spray pesticides in advance in the area where the diseases and insect pests are predicted to be received, the large-scale diffusion of the diseases and insect pests is avoided, the use and pollution of pesticides are reduced, the influence of the diseases and insect pests on the crop is reduced, the yield of the crop is improved, the efficiency of agricultural monitoring is improved, and the development of agriculture is promoted.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of the modular composition of an intelligent agricultural monitoring system based on machine vision according to the present invention;
FIG. 2 is a flow chart of steps of a machine vision-based agricultural intelligent monitoring method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: an agricultural intelligent monitoring method based on machine vision comprises the following steps:
s1, acquiring basic data information, acquiring crop surface image information through a camera, acquiring position information of the camera through a GPS, and performing encryption storage;
s2, analyzing and processing the surface image of the crop according to the acquired basic data information and image information, predicting the reason of plant diseases and insect pests of the crop, and providing advice;
in step S2, the following steps are included:
s201, performing binarization pretreatment on the collected crop surface image according to the collected crop surface image information and the position information of the camera equipment, and marking and dividing an agricultural area;
s202, extracting the outline of the surface of the crop in the image, comparing the outline with basic pest information stored in a database, and identifying the received pest situation to obtain a pest area distribution image, wherein the method comprises the following steps:
s202-1, calculating a two-dimensional distribution function f (x, y) through the following formula:
wherein (x, y) represents a maskPixel coordinates within the film, (x 0 ,y 0 ) Representing coordinates of a center point of a mask, wherein the mask of the image is used for shielding a processed image (whole or partial) by using a selected image, a graph or an object to control an area or a processing process of image processing, and Gaussian filtering is carried out on the image through the mask; sigma represents standard deviation; calculating coefficient values of each point in the mask through a two-dimensional distribution function to obtain convolution factors in eight directions;
s202-2, performing convolution operation on the preprocessed image and eight direction templates to obtain eight-direction contour information S 0° ，S 45 °，S 90 °，S 135 °，S 180° ，S 225° ，S 270° ，S 315° The point (x i ，y i ) Gray value H (x i ，y i ) And (3) performing calculation:
S 1 ＝S 0 ° 2 +S 45 ° 2 +S 90 ° 2 +S 135 ° 2 ；
S 2 ＝S 180 ° 2 +S 225 ° 2 +S 270 ° 2 +S 315 ° 2 ；
s202-3, setting the gray threshold value as H Threshold value The image contour gray value L (x, y) is calculated by the following formula:
when the gray value of the image contour is larger than or equal to 255, judging the image contour; otherwise, determining that the contour is non-contour;
s202-4, processing the extracted image contour through a region growing algorithm, wherein the region growing is a serial region segmentation image segmentation method. The region growth is to start from a certain pixel, gradually add adjacent pixels according to a certain criterion, and stop the region growth when a certain condition is met, so as to obtain the whole region and realize the extraction of the target;
the Jacquard distance s between the acquired profile image A and the basic pest image information B in the database is calculated by the following formula:
wherein α is denoted as a hyper-parameter; setting the threshold value as s Threshold value When s is>s Threshold value When s is less than or equal to s, the similarity of the representation images is low Threshold value When the similarity of the representation images is high; the plant diseases and insect pests are compared and identified, corresponding plant diseases and insect pests are obtained, control suggestions are given to users according to basic plant disease and insect pest control information, for example, sunlight of a ditch or a planting area near the area where the plant diseases and insect pests are generated is blocked, the position of the shooting equipment where the plant diseases and insect pests exist is marked according to the collected position information, and an agricultural electronic map is combined to obtain a plant disease and insect pest area distribution image;
s203, fitting the pest and disease area distribution image to obtain a crop pest and disease distribution situation image of the whole area, wherein the method comprises the following steps:
s203-1, smoothing the pest and disease damage area distribution image by the following formula:
Z′(a，b)＝h(a，b)*z(a，b)；
where a and b are represented as pixel values of the image, Z' (a, b) is represented as a smoothed output image, Z (a, b) is represented as an input image, and h (a, b) is represented as a smoothed convolution filter, which may be an average filter or a gaussian smoothing filter; noise interference can be removed by smoothing the image, and extra errors of detail texture edges in the pest and disease area distribution image can be reduced;
edge sharpening is performed on the smoothed image by the following formula:
Z(a，b)＝c|Z′(a，b)-Z′(a+1，b)|；
wherein Z (a, b) represents sharpening the output image, c represents template gain for highlighting the sharpened edge feature, the image sharpening is to compensate the outline of the image, enhance the edge and gray jump part of the image, and make the image clear;
s203-2, matching the processed images, and matching the adjacent image samples Z by the following formula 1 (a, b) and Z 2 The similarity distance D between (a, b) is calculated:
D＝(∑a＝0∑b＝0|Z 1 (n-1-N+a，b)-Z 2 (a，b)|)/N；
wherein N represents the number of columns searched in the image sample, and N represents the number of columns searched in the image sample; when D reaches the minimum value, the number N of search columns in the image sample is the same as the size of the overlapping area of the adjacent images, and the images are matched;
s203-3, performing image fitting on the pest and disease damage regional distribution map through the following formula:
P＝(1-β)P 1 +βP 2 ；
wherein P is expressed as an overall pest and disease damage distribution condition image, P 1 and P2 Adjacent image samples, denoted as having overlapping regions, β being denoted as a weighting factor;
s204, predicting the reason of the crop suffering from the plant diseases and insect pests according to the fitted integral plant diseases and insect pests distribution situation image, and suggesting the user.
S3, analyzing and processing the change trend of the crop subjected to the plant diseases and insect pests according to the analyzed crop surface image information;
in step S3, the following steps are included:
s301, building a disease and pest distribution prediction model, obtaining a fitted integral disease and pest distribution situation image according to time, dividing according to a dividing scale, obtaining a disease and pest distribution area, extracting gray values in each wave band to form a state matrix of the area, and marking the state matrix on the t-1 th day as U t-1 The state matrix on the t-th day is recorded as U t ；
S302, calculating the pest and disease damage change probability P from the t-1 day to the t day by the following formula:
U t ＝PU t-1 ；
wherein ,pγδ Expressed as the probability of the gray value changing from gamma to delta, p γδ Is non-negative, and the sum of each row is 1;
s303, predicting the distribution situation of the plant diseases and insect pests according to the probability prediction model to obtain a predicted plant diseases and insect pests distribution image.
And S4, displaying the user through a display device according to the analysis result, and reminding the user to take preventive and therapeutic measures as soon as possible through a voice device.
In step S4, the analysis result and the advice are displayed to the user through a screen display device, such as a mobile phone applet or a computer central control platform, and the user is reminded to take control measures as soon as possible through a voice device, such as a broadcast or alarm prompt tone, for example, spraying corresponding pesticides through an unmanned aerial vehicle or a pesticide sprayer in the predicted pest distribution area.
An agricultural intelligent monitoring system based on machine vision, the agricultural intelligent monitoring system comprising: the system comprises a data monitoring module, a database, a data analysis module and an agricultural guidance module;
the output end of the data monitoring module is connected with the input end of the database, the output end of the database is connected with the input end of the data analysis module, the output end of the data analysis module is connected with the input end of the agricultural guidance module, and the output end of the data analysis module is connected with the input end of the database;
the data monitoring module is used for collecting basic data information and carrying out image acquisition on the growth of plants, the data monitoring module comprises a basic data acquisition unit, an image acquisition unit and a position acquisition unit, the basic data acquisition unit is used for inputting agricultural basic data information, such as an agricultural electronic map, crop planting basic information, plant disease and insect pest information and the like, providing a comparison basis for image analysis processing, enabling analysis results to be more accurate, the image acquisition unit carries out image acquisition on the surfaces of crops through camera equipment, such as a high-definition camera, a remote sensing or plant growth detector and the like, and the position acquisition unit carries out position positioning on the camera equipment through a GPS.
The database is used for encrypting and storing collected data and analysis results, the database comprises a data encryption unit and a data storage unit, the data encryption unit encrypts the collected data and the analysis results through an SHA encryption algorithm, the safety and the privacy of the data are guaranteed, the SHA encryption algorithm is a safe hash algorithm, one-way hash algorithms of SHA-1, SHA-224, SHA-256, SHA-384 and SHA-512 are regulated, the SHA-1, SHA-224 and SHA-256 are suitable for messages with the length of not more than 2-64 binary bits, the SHA-384 and SHA-512 are suitable for messages with the length of not more than 2-128 binary bits, the hash is the refinement of information, the length of the hash is usually much smaller than that of the information, the hash with high confidentiality is necessarily irreversible, the result is that the original information of any part cannot be deduced, even if only one bit can cause obvious change of the hash result, namely whether the hash results have the same effect or not can be verified, and whether the hash results have the same anti-collision characteristics or not can be verified. The data storage unit stores collected data and analysis results through an HBase column database, wherein the HBase column database is a distributed, extensible and NoSQL database supporting mass data storage, a database supporting high-capacity storage and high-efficiency real-time query, and a database system with high reliability, high performance, column storage, scalability and real-time reading and writing is provided.
The data analysis module is used for analyzing and processing collected plant data, the data analysis module comprises an image analysis unit and a change analysis unit, the image analysis unit is used for analyzing and processing collected crop surface images, target crop images are obtained through data fitting, the reasons of diseases and insect pests generated by crops are predicted, suggestions for controlling are given, such as channels exist or the change analysis unit is used for analyzing and processing the change trend of the diseases and insect pests received by crops according to time change.
The agricultural guidance module is used for reminding and guiding a user according to an analysis result, the agricultural guidance module comprises a screen display unit and a voice reminding unit, the screen display unit displays the analysis result and the suggestion to the user through screen display equipment such as a mobile phone applet or a computer central control platform, the voice reminding unit is used for reminding the user to take prevention and treatment measures such as spraying corresponding pesticides and the like as soon as possible through voice equipment such as broadcasting or alarming prompt tones, the user is guaranteed to take measures against agricultural pest and disease problems quickly, large-scale damage is avoided, agricultural planting loss is reduced, the yield of agricultural production is improved, and the economic property safety of the user is guaranteed.
Example 1:
if the Jacquard distance between the acquired outline image A and the basic plant disease and insect pest image information B in the databaseIf s Threshold value When s is equal to or less than s =0.1 Threshold value The image similarity is high, the possibility that the current crop diseases and insect pests are consistent with the basic disease and insect pest image information B in the database is high, and advice is provided for users according to the control mode in the database;
combining the agricultural electronic map to obtain a disease and pest area distribution image, obtaining an overall disease and pest distribution situation image after image fitting, and if the state matrix U of the overall disease and pest distribution situation image is at t-1 days t-1 ，By passing throughObtaining a state matrix of an integral plant disease and insect pest distribution condition image on the t th dayAnd the whole plant diseases and insect pests distribution situation image on the t th day is obtained through prediction, the user is displayed through the display equipment, the prediction area is marked, and the prevention and treatment suggestion is given to the user.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. 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 (10)
1. An agricultural intelligent monitoring method based on machine vision is characterized by comprising the following steps of: comprises the following steps:
s1, acquiring basic data information, acquiring crop surface image information through a camera, acquiring position information of the camera through a GPS, and performing encryption storage;
s2, analyzing and processing the surface image of the crop according to the acquired basic data information and image information, predicting the reason of plant diseases and insect pests of the crop, and providing advice;
s3, analyzing and processing the change trend of the crop subjected to the plant diseases and insect pests according to the analyzed crop surface image information;
and S4, displaying the user through a display device according to the analysis result, and reminding the user to take preventive and therapeutic measures as soon as possible through a voice device.
2. The machine vision-based agricultural intelligent monitoring method as set forth in claim 1, wherein: in step S2, the following steps are included:
s201, performing binarization pretreatment on the collected crop surface image according to the collected crop surface image information and the position information of the camera equipment, and marking and dividing an agricultural area;
s202, extracting the outline of the surface of the crop in the image, comparing the outline with basic pest information stored in a database, and identifying the received pest situation to obtain a pest area distribution image, wherein the method comprises the following steps:
s202-1, calculating a two-dimensional distribution function f (x, y) through the following formula:
wherein (x, y) represents the pixel coordinates in the mask, (x) 0 ,y 0 ) Representing coordinates of a mask center point; sigma represents standard deviation; calculating coefficient values of each point in the mask through a two-dimensional distribution function to obtain convolution factors in eight directions;
s202-2, performing convolution operation on the preprocessed image and eight direction templates to obtain eight-direction contour information S 0° ，S 45° ，S 90° ，S 135° ，S 180° ，S 225° ，S 270° ，S 315° The point (x i ，y i ) Gray value H (x i ，y i ) And (3) performing calculation:
S 1 ＝S 0° 2 +S 45° 2 +S 90° 2 +S 135° 2 ；
S 2 ＝S 180° 2 +S 225° 2 +S 270° 2 +S 315° 2 ；
s202-3, setting the gray threshold value as H Threshold value The image contour gray value L (x, y) is calculated by the following formula:
when the gray value of the image contour is larger than or equal to 255, judging the image contour; otherwise, determining that the contour is non-contour;
s202-4, processing the extracted image contour through a region growing algorithm;
the Jacquard distance s between the acquired profile image A and the basic pest image information B in the database is calculated by the following formula:
wherein α is denoted as a hyper-parameter; setting the threshold value as s Threshold value When s is>s Threshold value When s is less than or equal to s, the similarity of the representation images is low Threshold value When the similarity of the representation images is high; and (3) comparing and identifying the plant diseases and insect pests to obtain corresponding plant diseases and insect pests, giving control suggestions to users according to basic plant diseases and insect pests control information, marking the position of the shooting equipment where the plant diseases and insect pests exist according to the collected position information, and combining with an agricultural electronic map to obtain a plant diseases and insect pests area distribution image.
3. The intelligent agricultural monitoring method based on machine vision according to claim 2, wherein: s203, fitting the pest and disease area distribution image to obtain a crop pest and disease distribution situation image of the whole area, wherein the method comprises the following steps:
s203-1, smoothing the pest and disease damage area distribution image by the following formula:
Z′(a，b)＝h(a，b)*z(a，b)；
where a and b are represented as pixel values of the image, Z' (a, b) is represented as a smoothed output image, Z (a, b) is represented as an input image, and h (a, b) is represented as a smoothed convolution filter;
edge sharpening is performed on the smoothed image by the following formula:
Z(a，b)＝c|Z′(a，b)-Z′(a+1，b)|；
wherein Z (a, b) represents the sharpened output image and c represents the template gain;
s203-2, matching the processed images, and matching the adjacent image samples Z by the following formula 1 (a, b) and Z 2 The similarity distance D between (a, b) is calculated:
D＝(∑ a＝0 ∑ b＝0 |Z 1 (n-1-N+a，b)-Z 2 (a，b)|)/N；
wherein N represents the number of columns searched in the image sample, and N represents the number of columns searched in the image sample; when D reaches the minimum value, the number N of search columns in the image sample is the same as the size of the overlapping area of the adjacent images, and the images are matched;
s203-3, performing image fitting on the pest and disease damage regional distribution map through the following formula:
P＝(1-β)P 1 +βP 2 ；
wherein P is expressed as an overall pest and disease damage distribution condition image, P 1 and P2 Adjacent image samples, denoted as having overlapping regions, β being denoted as a weighting factor;
s204, predicting the reason of the crop suffering from the plant diseases and insect pests according to the fitted integral plant diseases and insect pests distribution situation image, and suggesting the user.
4. A machine vision based agricultural intelligent monitoring method according to claim 3, characterized in that: in step S3, the following steps are included:
s301, building a disease and pest distribution prediction model, obtaining a fitted integral disease and pest distribution situation image according to time, dividing according to a dividing scale, obtaining a disease and pest distribution area, extracting gray values in each wave band to form a state matrix of the area, and marking the state matrix on the t-1 th day as U t-1 The state matrix on the t-th day is recorded as U t ；
S302, calculating the pest and disease damage change probability P from the t-1 day to the t day by the following formula:
U t ＝PU t-1 ；
wherein ,pγδ Expressed as the probability of the gray value changing from gamma to delta, p γδ Is non-negative, and the sum of each row is 1;
s303, predicting the distribution situation of the plant diseases and insect pests according to the probability prediction model to obtain a predicted plant diseases and insect pests distribution image.
5. The intelligent agricultural monitoring method based on machine vision according to claim 4, wherein: in step S4, the analysis result and the advice are displayed to the user through the screen display device, and the user is reminded to take preventive measures as soon as possible through the voice device.
6. Agricultural intelligent monitoring system based on machine vision, its characterized in that: this agricultural intelligent monitoring system includes: the system comprises a data monitoring module, a database, a data analysis module and an agricultural guidance module;
the output end of the data monitoring module is connected with the input end of the database, the output end of the database is connected with the input end of the data analysis module, the output end of the data analysis module is connected with the input end of the agricultural guidance module, and the output end of the data analysis module is connected with the input end of the database; the data monitoring module is used for collecting basic data information, carrying out image acquisition on plant growth, the database is used for carrying out encryption storage on collected data and analysis results, the data analysis module is used for carrying out analysis processing on the collected plant data, and the agricultural guidance module is used for reminding and guiding a user according to the analysis results.
7. The machine vision-based agricultural intelligent monitoring system of claim 6, wherein: the data monitoring module comprises a basic data acquisition unit, an image acquisition unit and a position acquisition unit, wherein the basic data acquisition unit is used for inputting agricultural basic data information, the image acquisition unit performs image acquisition on the surface of crops through camera equipment, and the position acquisition unit performs position positioning on the camera equipment through a GPS.
8. The machine vision-based agricultural intelligent monitoring system of claim 7, wherein: the database comprises a data encryption unit and a data storage unit, wherein the data encryption unit encrypts collected data and analysis results through an SHA encryption algorithm, and the data storage unit stores the collected data and the analysis results through an HBase column type database.
9. The machine vision-based agricultural intelligent monitoring system of claim 8, wherein: the data analysis module comprises an image analysis unit and a change analysis unit, wherein the image analysis unit is used for carrying out data analysis on the collected crop surface images, obtaining target crop images through data fitting, predicting the reasons of diseases and insect pests generated by crops and giving suggestions for controlling, such as the existence of a ditch or the presence of a change in time, and the change analysis unit is used for carrying out analysis processing on the change trend of the crop subjected to the diseases and insect pests.
10. The machine vision-based agricultural intelligent monitoring system of claim 9, wherein: the agricultural guidance module comprises a screen display unit and a voice reminding unit, wherein the screen display unit displays analysis results and suggestions to a user through screen display equipment, and the voice reminding unit is used for reminding the user to take preventive measures as soon as possible through voice equipment.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116934719A (en) * | 2023-07-26 | 2023-10-24 | 苏州德斯米尔智能科技有限公司 | Automatic detection system for belt conveyor |
| CN117057778A (en) * | 2023-08-15 | 2023-11-14 | 上海韦地科技集团有限公司 | Multifunctional camera data supervision system and method based on nuclear industry Internet of things |
| CN117291291A (en) * | 2023-08-12 | 2023-12-26 | 江苏信实环境工程有限公司 | Insect condition intelligent monitoring system and method based on Internet of things |
Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105787446A (en) * | 2016-02-24 | 2016-07-20 | 上海劲牛信息技术有限公司 | Smart agricultural insect disease remote automatic diagnosis system |
| US20160267675A1 (en) * | 2014-06-23 | 2016-09-15 | Boe Technology Group Co., Ltd. | Image edge detection method and apparatus thereof, image target identification method and apparatus thereof |
| CN106991619A (en) * | 2017-06-05 | 2017-07-28 | 河北哲瀚网络科技有限公司 | A kind of diseases and pests of agronomic crop intelligent diagnosis system and diagnostic method |
| CN109032093A (en) * | 2018-08-14 | 2018-12-18 | 荆州农业科学院 | A kind of rice pest monitoring system |
| KR102001398B1 (en) * | 2018-01-25 | 2019-07-18 | 재단법인 아산사회복지재단 | Method and apparatus for predicting brain desease change though machine learning and program for the same |
| CN110427922A (en) * | 2019-09-03 | 2019-11-08 | 陈�峰 | One kind is based on machine vision and convolutional neural networks pest and disease damage identifying system and method |
| CN110583594A (en) * | 2019-08-22 | 2019-12-20 | 合肥尘洁清洁技术服务有限公司 | Disease and pest early warning and prevention and control system |
| CN111539372A (en) * | 2020-05-06 | 2020-08-14 | 中南民族大学 | Method, equipment, storage medium and device for monitoring pest and disease damage distribution |
| KR20210000698A (en) * | 2018-12-05 | 2021-01-05 | (주)투비시스템 | A System for Growth Control of Crops and Disease and Insect Pest Control of Crops |
| CN112767381A (en) * | 2021-01-28 | 2021-05-07 | 陈一品 | Crop pest and disease identification method and system |
| KR20210088835A (en) * | 2020-01-07 | 2021-07-15 | 배철민 | Consulting system and method for handling damages by diseases and insects of farm produce |
| CN113406899A (en) * | 2021-06-09 | 2021-09-17 | 李仲先 | Intelligent agricultural monitoring system based on Internet of things |
| CN113837076A (en) * | 2021-09-24 | 2021-12-24 | 金陵科技学院 | Agricultural pest intelligent terminal analysis system |
| WO2022100034A1 (en) * | 2020-11-10 | 2022-05-19 | 广州柏视医疗科技有限公司 | Detection method for malignant region of thyroid cell pathological section based on deep learning |
| CN114782391A (en) * | 2022-04-29 | 2022-07-22 | 广州大学 | Method, system and device for constructing defect detection model of few-sample industrial image |
| KR20220115665A (en) * | 2021-02-08 | 2022-08-18 | 전북대학교산학협력단 | Apparatus for detecting the damage location of forest pests based on multifactorial images |
| CN114972843A (en) * | 2022-04-26 | 2022-08-30 | 黑龙江省农业科学院农业遥感与信息研究所 | Agricultural pest diagnosis and early warning system based on big data |
| CN115185220A (en) * | 2022-08-11 | 2022-10-14 | 安徽中昆绿色防控科技有限公司 | Agriculture and forestry disease and pest monitoring system based on thing networking |
| CN115376008A (en) * | 2022-08-23 | 2022-11-22 | 河南讯飞人工智能科技有限公司 | Method and device for identifying plant diseases and insect pests, electronic equipment and storage medium |
| KR20220160386A (en) * | 2021-05-27 | 2022-12-06 | (주)골든플래닛 | Method and system for managing crops pets |
| CN115482465A (en) * | 2022-09-20 | 2022-12-16 | 广东交通职业技术学院 | Crop disease and insect pest prediction method and system based on machine vision and storage medium |
| GB2610449A (en) * | 2021-09-06 | 2023-03-08 | Harbin Inst Technology | Efficient high-resolution non-destructive detecting method based on convolutional neural network |
-
2023
- 2023-03-21 CN CN202310276383.4A patent/CN116448760B/en active Active
Patent Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160267675A1 (en) * | 2014-06-23 | 2016-09-15 | Boe Technology Group Co., Ltd. | Image edge detection method and apparatus thereof, image target identification method and apparatus thereof |
| CN105787446A (en) * | 2016-02-24 | 2016-07-20 | 上海劲牛信息技术有限公司 | Smart agricultural insect disease remote automatic diagnosis system |
| CN106991619A (en) * | 2017-06-05 | 2017-07-28 | 河北哲瀚网络科技有限公司 | A kind of diseases and pests of agronomic crop intelligent diagnosis system and diagnostic method |
| KR102001398B1 (en) * | 2018-01-25 | 2019-07-18 | 재단법인 아산사회복지재단 | Method and apparatus for predicting brain desease change though machine learning and program for the same |
| US20200357120A1 (en) * | 2018-01-25 | 2020-11-12 | The Asan Foundation | Method and apparatus for predicting brain disease change through machine learning and program for the same |
| CN109032093A (en) * | 2018-08-14 | 2018-12-18 | 荆州农业科学院 | A kind of rice pest monitoring system |
| KR20210000698A (en) * | 2018-12-05 | 2021-01-05 | (주)투비시스템 | A System for Growth Control of Crops and Disease and Insect Pest Control of Crops |
| CN110583594A (en) * | 2019-08-22 | 2019-12-20 | 合肥尘洁清洁技术服务有限公司 | Disease and pest early warning and prevention and control system |
| CN110427922A (en) * | 2019-09-03 | 2019-11-08 | 陈�峰 | One kind is based on machine vision and convolutional neural networks pest and disease damage identifying system and method |
| KR20210088835A (en) * | 2020-01-07 | 2021-07-15 | 배철민 | Consulting system and method for handling damages by diseases and insects of farm produce |
| CN111539372A (en) * | 2020-05-06 | 2020-08-14 | 中南民族大学 | Method, equipment, storage medium and device for monitoring pest and disease damage distribution |
| WO2022100034A1 (en) * | 2020-11-10 | 2022-05-19 | 广州柏视医疗科技有限公司 | Detection method for malignant region of thyroid cell pathological section based on deep learning |
| CN112767381A (en) * | 2021-01-28 | 2021-05-07 | 陈一品 | Crop pest and disease identification method and system |
| KR20220115665A (en) * | 2021-02-08 | 2022-08-18 | 전북대학교산학협력단 | Apparatus for detecting the damage location of forest pests based on multifactorial images |
| KR20220160386A (en) * | 2021-05-27 | 2022-12-06 | (주)골든플래닛 | Method and system for managing crops pets |
| CN113406899A (en) * | 2021-06-09 | 2021-09-17 | 李仲先 | Intelligent agricultural monitoring system based on Internet of things |
| GB2610449A (en) * | 2021-09-06 | 2023-03-08 | Harbin Inst Technology | Efficient high-resolution non-destructive detecting method based on convolutional neural network |
| CN113837076A (en) * | 2021-09-24 | 2021-12-24 | 金陵科技学院 | Agricultural pest intelligent terminal analysis system |
| CN114972843A (en) * | 2022-04-26 | 2022-08-30 | 黑龙江省农业科学院农业遥感与信息研究所 | Agricultural pest diagnosis and early warning system based on big data |
| CN114782391A (en) * | 2022-04-29 | 2022-07-22 | 广州大学 | Method, system and device for constructing defect detection model of few-sample industrial image |
| CN115185220A (en) * | 2022-08-11 | 2022-10-14 | 安徽中昆绿色防控科技有限公司 | Agriculture and forestry disease and pest monitoring system based on thing networking |
| CN115376008A (en) * | 2022-08-23 | 2022-11-22 | 河南讯飞人工智能科技有限公司 | Method and device for identifying plant diseases and insect pests, electronic equipment and storage medium |
| CN115482465A (en) * | 2022-09-20 | 2022-12-16 | 广东交通职业技术学院 | Crop disease and insect pest prediction method and system based on machine vision and storage medium |
Non-Patent Citations (6)
| Title |
|---|
| CHING-JU CHEN: "An AIoT Based Smart Agricultural System for Pests Detection", 《SPECIAL SECTION ON DATA MINING FOR INTERNET OF THINGS》 * |
| YONGKANG YAO: "Pest Detection Crop Images Based on OTSU Algorithm and Deep Convolutional Neural Network", 《2020 INTERNATIONAL CONFERENCE ON VIRTUAL REALITY AND INTELLIGENT SYSTEMS (ICVRIS》 * |
| 沈同平: "改进的图像边缘检测算法的FPGA实现与仿真", 《安庆师范大学学报(自然科学版)》 * |
| 王翔宇: "农业主要病害检测与预警技术研究进展分析", 《农业机械学》 * |
| 胡孟娇: "基于图像处理的玉米常见病害识别研究", 《信息技术与信息化》 * |
| 郎瑶;: "一种改进的Sobel边缘检测算子", 广西轻工业, no. 05 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116934719A (en) * | 2023-07-26 | 2023-10-24 | 苏州德斯米尔智能科技有限公司 | Automatic detection system for belt conveyor |
| CN116934719B (en) * | 2023-07-26 | 2024-02-13 | 苏州德斯米尔智能科技有限公司 | Automatic detection system for belt conveyor |
| CN117291291A (en) * | 2023-08-12 | 2023-12-26 | 江苏信实环境工程有限公司 | Insect condition intelligent monitoring system and method based on Internet of things |
| CN117291291B (en) * | 2023-08-12 | 2024-04-23 | 江苏信实环境工程有限公司 | Insect condition intelligent monitoring system and method based on Internet of things |
| CN117057778A (en) * | 2023-08-15 | 2023-11-14 | 上海韦地科技集团有限公司 | Multifunctional camera data supervision system and method based on nuclear industry Internet of things |
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