CN117456214A - Tomato leaf spot identification method, system and electronic equipment - Google Patents
Tomato leaf spot identification method, system and electronic equipment Download PDFInfo
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Abstract
The invention discloses a tomato leaf spot recognition method, a tomato leaf spot recognition system and electronic equipment, and relates to the technical field of image recognition. The method is based on machine vision, mask processing is carried out on the acquired blade image, the edge contour of the blade is determined on the basis of the mask processing, and a rectangular image is obtained by adopting circumscribed rectangle cutting of the edge contour; the method comprises the steps of determining the global green average value of a leaf based on a rectangular image, traversing each pixel point in the rectangular image, determining the pixel point with the green value smaller than the sum of the global green average value and the deviation amount as a pixel point of a disease spot area, marking the pixel point of the disease spot area in the leaf image to accurately obtain the identification result of the disease spot of the leaf, providing scientific basis for reasonable medication of tomatoes, and further solving the problems of high labor intensity, pollution to land and planting personnel caused by excessive use of chemical fertilizer and reduced production benefit per unit area and the like in the prior art.
Description
Technical Field
The invention relates to the technical field of image recognition, in particular to a tomato leaf spot recognition method, a tomato leaf spot recognition system and electronic equipment.
Background
The tomatoes are rich in nutrition and excellent in taste, are popular among the public, but the tomatoes grow with preference to wet and heat and short sunlight, are suitable for insect damage under the environment, so that the tomatoes are extremely susceptible to diseases such as powdery mildew and late blight, the diseases are strong in transmissibility in the tomatoes, and great losses are brought to tomato planting once the diseases occur.
Late blight is caused by fungi, leaves and fruits are generally affected first, the tomato leaves can be provided with disease spots, human eyes can easily distinguish the disease spots, the disease spots can be prolonged to main stems, and finally the whole tomato is dead. The white fly can gnaw tomato leaves and main stems, and is harmful per se, but the white fly is an excellent virus carrier, and the white fly can transmit viruses to plants through gnawing, so that the white fly is infected. After infection of viruses, tomatoes can show leaf spots, leaf curl, top yellowing and the like, so that the whole tomato plant reduces or stops producing. Based on the method, according to the condition of the disease spots of the growth of the leaves of the tomato plants, whether the tomatoes are infected, the infection degree, the drug application amount and the like can be judged, and the prior art generally carries out artificial judgment through the growers, regularly carries out drug application control on the tomatoes, and the problems of high labor intensity, pollution to the land and the growers caused by excessive use of chemical fertilizer and pesticide, reduced production benefit in unit area and the like are caused in the tomato planting process.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a tomato leaf spot recognition method, a tomato leaf spot recognition system and electronic equipment.
In order to achieve the above object, the present invention provides the following solutions:
a method for identifying tomato leaf lesions, comprising:
acquiring a leaf image of a tomato to be identified;
performing mask processing on the blade image to obtain a masked image;
determining the edge contour of the blade based on the image after masking, and cutting the blade image by adopting an external rectangle of the edge contour to obtain a rectangular image;
determining a global green mean value of the blade based on the rectangular image;
acquiring a green value of each pixel point in the rectangular image;
determining a pixel point with a green value smaller than the sum of the global green average value and the deviation value as a pixel point of a lesion area;
and marking the pixel points of the lesion area in the rectangular image to obtain a leaf lesion recognition result.
Optionally, performing mask processing on the blade image to obtain a masked image, which specifically includes:
extracting the boundary of the blade in the blade image;
determining the area of a boundary communication area, and taking the boundary corresponding to the maximum boundary communication area as an outline;
setting the pixel values of the pixel points outside the outline to be zero, and setting the values of the pixel points inside the outline and the outline to be 255, so as to obtain a processed image;
and performing logic AND operation on the processed image and the blade graph to obtain a masked image.
Optionally, determining an edge contour of the blade based on the image after masking, and clipping the blade image by adopting a circumscribed rectangle of the edge contour to obtain a rectangular image, which specifically comprises:
performing binarization processing on the image after the mask to obtain a binarized image;
performing edge extraction on the binarized image to obtain an edge contour;
and carrying out circumscribed rectangle marking on the edge outline, and cutting the blade image by adopting the marked circumscribed rectangle to obtain the rectangular image.
Optionally, the global green average is:
G avg ＝G total /(row*col)；
wherein G is total Representing the global pixel point green total value,g (r, c, 1) represents the green value of the pixel point of the r th row and the c th column in the rectangular image, G avg The global green average value is represented, row represents the total number of rows of pixels in the rectangular image, and col represents the total number of columns of pixels in the rectangular image.
Optionally, the deviation value is corrected by adopting a supervised learning method of loop iteration.
Optionally, the process of correcting and obtaining the deviation value by adopting a supervised learning method of loop iteration is as follows:
obtaining a new deviation value by adopting a formula C' =η×c+ε; wherein, C' is a new deviation value, eta is a learning rate, epsilon is a recursion error, and C is an initial deviation value;
updating the recursive error; the updated recursive error is ε': ε '=c—c';
and carrying out iterative calculation by taking the new deviation value as an initial deviation value of the next iteration until the recursion error is smaller than a set critical value, and taking the new deviation value determined in the last iteration as the deviation value.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the method is based on machine vision, mask processing is carried out on the acquired blade image, the edge contour of the blade is determined on the basis of the mask processing, and a rectangular image is obtained by adopting circumscribed rectangle cutting of the edge contour; the method comprises the steps of determining the global green average value of a leaf based on a rectangular image, traversing each pixel point in the rectangular image, determining the pixel point with the green value smaller than the sum of the global green average value and the deviation amount as a pixel point of a disease spot area, marking the pixel point of the disease spot area in the leaf image to accurately obtain the identification result of the disease spot of the leaf, providing scientific basis for reasonable medication of tomatoes, and further solving the problems of high labor intensity, pollution to land and planting personnel caused by excessive use of chemical fertilizer and reduced production benefit per unit area and the like in the prior art.
Further, the invention provides a tomato leaf spot recognition system, which is used for implementing the tomato leaf spot recognition method; the system comprises:
the image acquisition module is used for acquiring leaf images of tomatoes to be identified;
the mask processing module is used for performing mask processing on the blade image to obtain a masked image;
the cutting image module is used for determining the edge contour of the blade based on the image after masking, and cutting the blade image by adopting the circumscribed rectangle of the edge contour to obtain a rectangular image;
the average value determining module is used for determining the global green average value of the blade based on the rectangular image;
the green value determining module is used for obtaining the green value of each pixel point in the rectangular image;
the disease spot determining module is used for determining pixel points with green values smaller than the sum of the global green average value and the deviation amount as pixel points of a disease spot area;
and the disease spot identification module is used for marking the disease spot area pixel points in the rectangular image to obtain a leaf disease spot identification result.
Still further, the present invention also provides an electronic device including:
a memory for storing a computer program;
and the processor is connected with the memory and is used for calling and executing the computer program so as to implement the tomato leaf spot recognition method.
The technical effects achieved by the two implementation structures provided by the invention are the same as those achieved by the tomato leaf spot recognition method provided by the invention, so that the description is omitted here.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for identifying a spot on a tomato leaf part;
FIG. 2 is a schematic diagram of a masked image according to an embodiment of the present invention;
FIG. 3 is a schematic view of a rectangular positioned blade portion according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the lesion marking results provided by 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.
The invention aims to provide a tomato leaf spot recognition method, a tomato leaf spot recognition system and electronic equipment, which can realize accurate recognition of tomato leaf spots based on machine vision, thereby providing scientific basis for reasonable medicine use of tomatoes.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the method for identifying the tomato leaf spot provided by the invention comprises the following steps:
step 100: and obtaining leaf images of tomatoes to be identified.
Step 101: and performing mask processing on the blade image to obtain a masked image.
In the practical application process, the boundary of the blade image is extracted. And calculating the area of the boundary communication area, reserving the boundary with the largest area as an edge contour, and neglecting the rest boundaries. All pixels outside the edge contour are set to be black (0, 0), all pixels inside the edge contour and the edge contour are set to be white (255 ), and the processed image and the acquired blade image are subjected to logic AND operation to obtain a masked image, namely a region of interest (region ofinterest, ROI). Wherein the masked image is shown in fig. 2.
Step 102: and determining the edge contour of the blade based on the image after masking, and cutting the blade image by adopting the circumscribed rectangle of the edge contour to obtain a rectangular image. The rectangular image obtained by clipping is shown in fig. 3.
In the practical application process, the implementation process of the step can be as follows:
step 1021: and performing binarization processing on the image after the mask to obtain a binarized image.
Step 1022: and carrying out edge extraction on the binarized image to obtain an edge contour. And taking the calculated maximum contour as an edge contour.
Step 1023: and carrying out circumscribed rectangle marking on the edge outline, and cutting the blade image by adopting the marked circumscribed rectangle to obtain a rectangular image. This step is mainly to reduce the amount of computation at the time of the subsequent global loop.
Based on the above description, the implementation of step 102 is essentially to rectangular position the outline of the core blade after it is calculated to obtain the blade portion to be processed.
Step 103: a global green average of the blades is determined based on the rectangular image. The global green average value is:
G avg ＝G total /(row*col)。
wherein G is total Representing the global pixel point green total value,g (r, c, 1) represents the green value of the pixel point of the r th row and the c th column in the rectangular image, G avg Represents the global green average value, row represents the total line number of pixel points in a rectangular image, and col represents the total number of columns of pixels in the rectangular image. 1 in G (r, c, 1) represents that the color value of the pixel to be extracted is green.
Step 104: and obtaining a green value of each pixel point in the rectangular image.
Step 105: and determining the pixel point with the green value smaller than the sum of the global green average value and the deviation amount as the pixel point of the lesion area. In the step, in the process of judging the leaf spot area by using the global green average value and the deviation value, comparing the relation between the green value of the pixel point and the sum of the global green average value and the deviation value pixel by pixel point. The judging formula of the pixel points in the disease spot area is as follows:
G＜G avg +C；
wherein G is the green value of the pixel, and C' is the offset value.
Furthermore, in order to improve the accuracy of disease spot identification, the invention corrects the deviation by using a supervised learning method of loop iteration, and the specific correction process is as follows:
1) A new deviation value is obtained using the formula C' =η c+epsilon. Wherein, C' is a new deviation value, eta is a learning rate, epsilon is a recursive error, and C is an initial deviation value.
2) Updating the recursive error. The updated recursive error is ε': ε '=c-C'.
3) And after the single judgment is successful, carrying out iterative calculation by taking the new deviation value as an initial deviation value of the next iteration, ending the iteration (namely ending training) until the recursion error is smaller than a set critical value, and taking the new deviation value determined in the last iteration as a deviation value C.
In the practical application process, multiple experiments prove that the value of the learning rate eta is 0.95, and the initial deviation value C is (255-G avg )/2，ε<And 4, stopping iteration, carrying out statistical analysis on the deviation values of the images, removing unstable points with larger variances, taking an average value, and obtaining final deviation to finish deviation correction.
Step 106: and marking the pixel points of the lesion area in the rectangular image to obtain a leaf lesion recognition result. The leaf spot recognition result is shown in fig. 4.
Based on the description, the color change of the spot area of the tomato leaf is larger, and spot identification can be stably realized by using the point-by-point color, the area color average value and the global color average value as color features.
Further, the invention provides a tomato leaf spot recognition system, which is used for implementing the tomato leaf spot recognition method. The system comprises: the device comprises an image acquisition module, a mask processing module, a clipping image module, a mean value determining module, a green value determining module, a disease spot determining module and a disease spot identifying module.
And the image acquisition module is used for acquiring the leaf image of the tomato to be identified.
And the mask processing module is used for performing mask processing on the blade image to obtain a masked image.
And the cutting image module is used for determining the edge contour of the blade based on the image after the mask, and adopting the circumscribed rectangle cutting blade image of the edge contour to obtain a rectangle image.
And the average value determining module is used for determining the global green average value of the blade based on the rectangular image.
And the green value determining module is used for acquiring the green value of each pixel point in the rectangular image.
And the spot determining module is used for determining the pixel point with the green value smaller than the sum of the global green average value and the deviation amount as the spot area pixel point.
And the disease spot identification module is used for marking the disease spot area pixel points in the rectangular image to obtain a leaf disease spot identification result.
Still further, the present invention also provides an electronic device including: memory and a processor.
The memory is used for storing a computer program.
The processor is connected with the memory for retrieving and executing the computer program to implement the tomato leaf spot recognition method.
Furthermore, the computer program in the above-described memory may be stored in a computer-readable storage medium when it is implemented in the form of a software functional unit and sold or used as a separate product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. A method for identifying a tomato leaf spot, comprising:
acquiring a leaf image of a tomato to be identified;
performing mask processing on the blade image to obtain a masked image;
determining the edge contour of the blade based on the image after masking, and cutting the blade image by adopting an external rectangle of the edge contour to obtain a rectangular image;
determining a global green mean value of the blade based on the rectangular image;
acquiring a green value of each pixel point in the rectangular image;
determining a pixel point with a green value smaller than the sum of the global green average value and the deviation value as a pixel point of a lesion area;
and marking the pixel points of the lesion area in the rectangular image to obtain a leaf lesion recognition result.
2. The method for identifying the leaf spot of the tomato according to claim 1, wherein the step of masking the leaf image to obtain a masked image comprises:
extracting the boundary of the blade in the blade image;
determining the area of a boundary communication area, and taking the boundary corresponding to the maximum boundary communication area as an outline;
setting the pixel values of the pixel points outside the outline to be zero, and setting the values of the pixel points inside the outline and the outline to be 255, so as to obtain a processed image;
and performing logic AND operation on the processed image and the blade graph to obtain a masked image.
3. The method for identifying the leaf spot of the tomato according to claim 1, wherein the edge contour of the leaf is determined based on the image after masking, and the leaf image is cut by adopting a circumscribed rectangle of the edge contour, so as to obtain a rectangular image, and specifically comprising the following steps:
performing binarization processing on the image after the mask to obtain a binarized image;
performing edge extraction on the binarized image to obtain an edge contour;
and carrying out circumscribed rectangle marking on the edge outline, and cutting the blade image by adopting the marked circumscribed rectangle to obtain the rectangular image.
4. The method for identifying tomato leaf lesions as defined in claim 1, wherein the global green average is:
G avg ＝G total /(row*col)；
wherein G is total Representing the global pixel point green total value,g (r, c, 1) represents the green value of the pixel point of the r th row and the c th column in the rectangular image, G avg The global green average value is represented, row represents the total number of rows of pixels in the rectangular image, and col represents the total number of columns of pixels in the rectangular image.
5. The tomato leaf spot recognition method of claim 1, wherein the deviation value is corrected using a supervised learning method of loop iteration.
6. The method for identifying the leaf lesions of the tomato according to claim 5, wherein the process of correcting the deviation value by using a supervised learning method of cyclic iteration comprises the following steps:
obtaining a new deviation value by adopting a formula C' =η×c+ε; wherein, C' is a new deviation value, eta is a learning rate, epsilon is a recursion error, and C is an initial deviation value;
updating the recursive error; the updated recursive error is ε': ε '=c—c';
and carrying out iterative calculation by taking the new deviation value as an initial deviation value of the next iteration until the recursion error is smaller than a set critical value, and taking the new deviation value determined in the last iteration as the deviation value.
7. A tomato leaf spot recognition system, characterized in that the system is adapted to implement a tomato leaf spot recognition method as claimed in any one of claims 1-6; the system comprises:
the image acquisition module is used for acquiring leaf images of tomatoes to be identified;
the mask processing module is used for performing mask processing on the blade image to obtain a masked image;
the cutting image module is used for determining the edge contour of the blade based on the image after masking, and cutting the blade image by adopting the circumscribed rectangle of the edge contour to obtain a rectangular image;
the average value determining module is used for determining the global green average value of the blade based on the rectangular image;
the green value determining module is used for obtaining the green value of each pixel point in the rectangular image;
the disease spot determining module is used for determining pixel points with green values smaller than the sum of the global green average value and the deviation amount as pixel points of a disease spot area;
and the disease spot identification module is used for marking the disease spot area pixel points in the rectangular image to obtain a leaf disease spot identification result.
8. An electronic device, comprising:
a memory for storing a computer program;
a processor, connected to the memory, for retrieving and executing the computer program to implement the tomato leaf spot recognition method according to any one of claims 1-6.
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