CN109308697A - A kind of leaf disease recognition method based on machine learning algorithm - Google Patents

A kind of leaf disease recognition method based on machine learning algorithm Download PDF

Info

Publication number
CN109308697A
CN109308697A CN201811087765.8A CN201811087765A CN109308697A CN 109308697 A CN109308697 A CN 109308697A CN 201811087765 A CN201811087765 A CN 201811087765A CN 109308697 A CN109308697 A CN 109308697A
Authority
CN
China
Prior art keywords
image
value
machine learning
learning algorithm
leaf disease
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201811087765.8A
Other languages
Chinese (zh)
Inventor
王兵
卢琨
周郁明
程木田
陈鹏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Anhui University of Technology AHUT
Original Assignee
Anhui University of Technology AHUT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Anhui University of Technology AHUT filed Critical Anhui University of Technology AHUT
Priority to CN201811087765.8A priority Critical patent/CN109308697A/en
Publication of CN109308697A publication Critical patent/CN109308697A/en
Pending legal-status Critical Current

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KGRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for recognising patterns
    • G06K9/62Methods or arrangements for pattern recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G06K9/6269Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on the distance between the decision surface and training patterns lying on the boundary of the class cluster, e.g. support vector machines
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/10Segmentation; Edge detection
    • G06T7/11Region-based segmentation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/10Segmentation; Edge detection
    • G06T7/136Segmentation; Edge detection involving thresholding
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/90Determination of colour characteristics
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/40Extraction of image or video features
    • G06V10/56Extraction of image or video features relating to colour
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30181Earth observation
    • G06T2207/30188Vegetation; Agriculture

Abstract

The leaf disease recognition method based on machine learning algorithm that the invention discloses a kind of, belongs to technical field of image processing.The present invention carries out the pretreatment operations such as gray processing, image enhancement and denoising to the leaf sample image of acquisition first;Then pretreated image is split by adaptive thresholding algorithm, effectively characterizes the texture information of sample image;It selects RGB color to extract the color characteristic of sample image, while extracting the textural characteristics of segmented image according to gray level co-occurrence matrixes;Finally selection supporting vector machine model carries out Classification and Identification to sample image using cross validation algorithm, is first optimized using grid optimizing method to the major parameter of SVM model, then choose the optimal parameter of recognition accuracy and establish svm classifier identification model.The present invention can enable a computer to the pest and disease damage of automatic identification leaf by training, greatly reduce room and time expense, also improve the precision of identification, have the characteristics that quick, accurate and strong robustness.

Description

A kind of leaf disease recognition method based on machine learning algorithm
Technical field
The present invention relates to computerized information technical field of image processing, are based on machine learning more specifically to one kind The leaf disease recognition method of algorithm.
Background technique
Quickly increase along with the continuous high speed development and population of China's economy, the agricultural in China also develops accordingly Get up, fruit has become indispensable food in common people family, and since fruit demand is very big, its cultivated area is also constantly expanding Greatly.With the increase of Production of fruit area, its disease problem also gradually causes the concern and attention of expert, correctly efficiently knows The Damage Types of other fruit tree can be such that fruit tree is timely treated, to reduce unnecessary loss, increase the yield in orchard.
Instantly, image processing techniques is developed rapidly, while the application in life and production is also gradually popularized.Then Image recognition technology is in agriculture field using but also the improvement and prevention and treatment of corps diseases achieve great improvement.To fruit The disease geo-radar image for setting leaf carries out rapid identification, is mainly concerned with special to Image Acquisition, image preprocessing, image segmentation, image Sign extraction, image classification identification etc. are studied and are realized, and achieve good achievement.
Through retrieving, MohammedE1-Helly etc. (2004) develops a comprehensive image processing system, can be automatic Tikka is detected to determine disease type.The system uses artificial neural network as classifier to identify powdery mildew of cucumber, downy mildew Disease and leaf pest.But this method calculation amount is larger, and time loss is more, and algorithm performance is poor.
2015, Chinese Patent Application No.: CN104422660A, publication date were on March 18th, 2015, are disclosed a kind of high The plant leaf blade pest diagnosis system of spectrum, including hyperspectral imager, camera lens, calibration blank, sample placement platform, linear light Source, linear light source supporting wall, automatically controlled displacement platform, mobile platform control device, camera bellows, adjustable support foot and computer.EO-1 hyperion at As instrument and linear light source are connected in together on automatically controlled displacement platform, are controlled and moved together by mobile platform control device;In moving process Middle linear light source irradiation calibration blank and measured leaf, hyperspectral imager acquisition data simultaneously upload to computer in real time;Through After crossing image analysis and processing software processing, the triple diagnostic models of the pest and disease damage having built up are inputted, real-time quantitative obtains plant Pest and disease damage gradient of infection, whether analysis decision to measurement plant spray and types of medicines dosage and nozzle type accordingly.It should Application case is mainly used for the pest and disease damage gradient of infection of rapidly diagnosis of plant, to level of decision-making, the realization essence for improving accuracy pesticide applying Thin agricultural plays positive effect.But the shortcoming of this application is, can not distinguish to different pest and disease damages.
2015, Chinese Patent Application No. CN201510321137.1, the applying date was on June 11st, 2015, innovation and creation Title are as follows: fusion spectrum and the crop disease and insect of image information identify and distinguish between method, and this application is based on spectrum and image is believed Cease processing technique and obtain spectral image data, carry out leaf area and background separation later, disease pest blemish extracts, spectral signature and Box counting algorithm.The pest and disease damage type of final Shi Yongfeishi linear discriminant analysis model output blade.This application can have Effect ground identifies different type pest and disease damage, for applications of pesticide management and pest and disease damage green prevention and control provide science decision according to According to.But spectrum picture used in this application needs professional's acquisition and equipment is expensive, is unfavorable for promoting the use.
Summary of the invention
1. technical problems to be solved by the inivention
The method of traditional fruit tree leaf disease management relies primarily on orchard worker or agricultural experts in person to checking in orchard The upgrowth situation of fruit tree, this method judges the upgrowth situation and disease of fruit tree by human eye, thus the result identified can exist Certain error, while also taking time and effort.In order to overcome the above problem, the invention proposes a kind of based on machine learning algorithm Leaf disease recognition method;The present invention can significantly be reduced by Computer Automatic Recognition different type fruit tree leaf disease Room and time expense, while also improving accuracy of identification;Irregular for the sample image quality of acquisition, characteristics of image is not Enough prominent, the present invention pre-processes sample image, to make sample convenient for the progress of follow-up work;For sample image The texture difference of performance is unobvious, is not easy to texture feature extraction, and the present invention uses adaptive threshold to pretreated image Algorithm carries out image segmentation, to highlight the textural characteristics of sample image well;For support vector machines (SVM) model silent Recognize under parameter and be unable to satisfy identification requirement, present invention combination cross validation algorithm obtains SVM model most using grid optimizing method Excellent parameter prevents from providing support to reach optimum efficiency for fruit disease evil.
2. technical solution
In order to achieve the above objectives, technical solution provided by the invention are as follows:
A kind of leaf disease recognition method based on machine learning algorithm of the invention, the steps include:
1) collect leaves sample image, collects healthy image and each 100 width of disease geo-radar image altogether, and located in advance to image Reason;
2) pretreated sample image is split using adaptive threshold fuzziness method, the texture of prominent sample image Feature;
3) color characteristic of sample image and the textural characteristics of segmented image are extracted based on RGB color;
4) feature for extracting all sample images is set as sample characteristics collection m, constructs SVM using the method for cross validation Disaggregated model;
5) optimized parameter is found using grid optimizing method, the parameter is penalty factor and nuclear parameter g;
6) optimized parameter found using step 5), can be obtained optimal svm classifier model, and test sample image is defeated Enter to trained svm classifier model, obtains recognition result.
Further, image preprocessing process described in step 1) are as follows: size tune is carried out to sample image size first The processing of whole and gray processing is adjusted having a size of 100*100, then carries out image increasing to gray level image using algorithm of histogram equalization Strong operation finally carries out denoising to image using median filtering algorithm.
Further, the image enhancement operation process are as follows: first simultaneously by the grey scale pixel value normalization of original image Each gray-scale distribution probability is counted, then normalized gray value convert and according to identical using accumulation transforming function transformation function Gray level be converted into the gray value of standard, to obtain equalisedization treated new images.
Further, the process that step 2) is split sample image are as follows:
Setting gray value of the image at pixel (a, b) is k (a, b), B × B window centered on pixel (a, b) Mouthful, wherein B indicates the side length of window, and C indicates difference, calculates the threshold value v (a, b) of each pixel (a, b) in image, calculation formula It is as follows:
Binaryzation is carried out point by point with v (a, b) value to pixel (a, b) each in image:
Further, color characteristic described in step 3) include the mean value in tri- channels RGB, variance, the degree of bias, kurtosis, Six features of entropy and gradient, textural characteristics include angular second moment ASM, entropy ENT, contrast C ON and unfavourable balance battle array IDM.
Further, color characteristic described in step 3) is expressed as follows:
A. mean value:
B. variance:
C. the degree of bias:
D. kurtosis:
E. entropy:
F. gradient:
In above formula, p (k) indicates the distribution probability that grey scale pixel value is k in the picture, nkFor its frequency, n is that pixel is total Number, L is number of greyscale levels,For mean value, δ2For variance, kkFor the degree of bias, kFFor kurtosis, kEFor entropy, M × N indicates the size of image,Indicate that gray value is the pixel gradient in the horizontal direction of k,Gradient of the pixel in vertical direction that expression gray value is k, kgFor its average gradient.
Further, textural characteristics described in step 3) are expressed as follows:
A. angular second moment (ASM):
B. entropy (ENT):
C. contrast (CON):
D. unfavourable balance battle array (IDM):
Wherein, G (i, j) is the element in gray level co-occurrence matrixes, and L is number of greyscale levels.
Further, the kernel function of svm classifier model described in step 4) is Radial basis kernel function:
Wherein, nuclear parameter g represents the width of kernel function.
Further, the default value of penalty factor described in step 5) and nuclear parameter g are respectively 500 and 0.5.
Further, the optimal value of the penalty factor and nuclear parameter g that search out in step 5) is 7000 and 0.006.
3. beneficial effect
Using technical solution provided by the invention, compared with existing well-known technique, there is following remarkable result:
(1) a kind of leaf disease recognition method based on machine learning algorithm of the invention, during image preprocessing Gray processing and image enhancement processing are carried out to the image after size adjusting first, then image is carried out using median filtering algorithm Denoising.After the processing, it can reinforce image perception with improving image quality, rich image information and improve image knowledge Other effect.
(2) a kind of leaf disease recognition method based on machine learning algorithm of the invention, using certainly in image segmentation Adapt to Threshold Segmentation Algorithm can well by sample image healthy image and disease geo-radar image effectively distinguish texture expression is upper, By image segmentation, the texture feature extraction for subsequent sample image provides advantage.
(3) a kind of leaf disease recognition method based on machine learning algorithm of the invention is based on RGB in feature extraction Color space extracts the color characteristic of sample image, and generates gray level co-occurrence matrixes with the sample image after segmentation, to extract Its textural characteristics converts sample image to the characteristic value of characterization image by feature extraction, classifies for subsequent sample image The training of model provides necessary condition.
(4) a kind of leaf disease recognition method based on machine learning algorithm of the invention, passes through friendship in image recognition Fork verification algorithm is trained and tests to SVM model, and the optimized parameter of SVM model is obtained using grid optimizing method, passes through After the training, optimal recognition effect can achieve.
Detailed description of the invention
Fig. 1 is a kind of flow chart of leaf disease recognition method based on machine learning algorithm of the invention;
Fig. 2 is apple leaf gray level image (disease geo-radar image) described in the embodiment of the present invention 1;
Fig. 3 is the reinforcing effect figure of Fig. 2 image;
Fig. 4 is the denoising image after Fig. 3 median filtering;
Fig. 5 is the effect picture after Fig. 4 adaptive threshold fuzziness;
Fig. 6 is the preprocessed effect picture with after adaptive threshold fuzziness of Fig. 2;
Fig. 7 is that this disaggregated model is judged as sick effect picture;
Fig. 8 is the effect picture that this disaggregated model is judged as health.
Specific embodiment
To further appreciate that the contents of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments.
Embodiment 1
In conjunction with Fig. 1, a kind of leaf disease recognition method based on machine learning algorithm of the present embodiment, to apple leaf For carrying out disease recognition, the step of identifying to disease geo-radar image, is as follows:
1) image preprocessing: acquisition apple leaf sample image, the sample image include health and disease geo-radar image, disease Containing disease region and certain healthy area in image, as long as the sample image containing disease region can regard as disease figure Picture, the present embodiment has collected healthy image and each 100 width of disease geo-radar image altogether, and pre-processes to these images.
Described image preprocessing process are as follows: size adjusting is carried out to sample image size first and gray processing is handled, is obtained Grayscale image as shown in Figure 2 adjusts having a size of 100*100, then carries out figure to gray level image using algorithm of histogram equalization Image intensifying operation, the image enhancement operation process are as follows:
It normalizes the grey scale pixel value k of original image to obtain r firstkAnd calculate each gray-scale distribution probability p (rk), so Afterwards using accumulation transforming function transformation function T () to normalized gray value rkIt is converted to obtain skAnd according to identical gray level by its The gray value of standard is converted to, to obtain equalisedization treated new images, carries out effect such as Fig. 3 of image enhancement acquisition It is shown, the contrast of image, the details of rich image can be improved after above-mentioned processing.The accumulation transforming function transformation function is as follows:
N is sum of all pixels, n in above formulajFor the number of pixels of different grey-scale, L is number of greyscale levels, j=0,1 ..., k.
Denoising is finally carried out using median filtering algorithm, denoising image as shown in Figure 4 is obtained, by denoising Afterwards, while filtering out noise, the details and profile of image are remained to the greatest extent.
2) image segmentation:
It is k (a, b) that gray value of the image at pixel (a, b) is set in partitioning algorithm, with pixel (a, b) is B × B window of the heart, wherein B indicate window side length, C indicate difference, calculate image in each pixel (a, b) threshold value v (a, B), calculation formula is as follows:
Binaryzation is carried out point by point with v (a, b) value to pixel (a, b) each in image:
Adaptive threshold fuzziness is carried out to the image after denoising and obtains effect picture as shown in Figure 5 and to healthy image warp Step 1) and step 2) obtain effect picture as shown in FIG. 6.From Fig. 5 and Fig. 6 comparison as can be seen that the segmented image texture of health It is evenly distributed, texture primitive is smaller, and the segmented image grain distribution entanglement of disease, and texture primitive is larger, this shows sample graph As after step 1) and step 2) textural characteristics by Efficient Characterization.
3) image characteristics extraction: after being pre-processed using domain-adaptive Threshold Segmentation Algorithm described in step 2) to step 1) Apple leaf image be split, highlight the textural characteristics of healthy image and disease geo-radar image.
It is primarily based on RGB color, the color moment in the color characteristic of apple leaf disease geo-radar image is selected, according to apple Gray value after the normalization of leaf disease geo-radar image extracts R, the mean value in tri- channels G, B, variance, the degree of bias, kurtosis, entropy, ladder respectively Spend six characteristic values, each characteristic value, the characteristics of picture is all represented in Digital Image Processing in a certain respect.Then root According to image brightness distribution rule and the pixel space position feature of similar luminance, counted in the sample image after step 3) segmentation According to certain orientation and distance and gray value is respectively that the frequency that occurs of combination of pixels of i-1 and j-1 is denoted as G (i, j), wherein i =1,2 ..., L, j=1,2 ..., L, and then generate gray level co-occurrence matrixes G, thus extract its second moment (ASM), entropy (ENT), The textural characteristics such as contrast (CON) and unfavourable balance battle array (IDM).
Six color features are as follows:
A. mean value (Mean): reflecting the brightness of image, and mean value is bigger to illustrate that brightness of image is bigger, otherwise smaller.
B. variance (Variance): being a kind of scale calibration for measuring sample distribution uniformity.
C. the symmetry of sample totality value distribution, the i.e. torsion resistance of image the degree of bias (Skewness): are described.
D. kurtosis (Kurtosis): describe sample peak value is precipitous or gradual degree.
E. entropy (Entropy): it characterizes the information content that the aggregation characteristic of intensity profile in image is included.
F. gradient (Gradient): the case where describing the variation speed of gray value of image.
Wherein, p (k) indicates the distribution probability that grey scale pixel value is k in the picture, nkFor its frequency, n is sum of all pixels, For mean value, L is number of greyscale levels, δ2For variance, kkFor the degree of bias, kFFor kurtosis, kEFor entropy, M × N indicates the size of image,It indicates Gray value is the pixel gradient in the horizontal direction of k,Expression gray value is gradient of the pixel of k in vertical direction, kgFor it Average gradient.
Four textural characteristics are described as follows:
A. angular second moment (ASM): the width for measuring the whether uniform texture of intensity profile, when image texture is relatively narrow, When intensity profile is uniform, value is larger, conversely, smaller.
B. entropy (ENT): the information content and information complexity that description image has, when complexity is high, entropy is larger, It is on the contrary then smaller.
C. contrast (CON): the measurement between description piece image gray value is minimum and maximum refers to the contrast of gray value Situation.Contrast is bigger, and representative comparison is bigger, and the smaller representative comparison of contrast is smaller.
D. unfavourable balance battle array (IDM): the distinguishable degree of description image texture and whether period presentation.It is easy to differentiate, regularity is strong , then value is big, otherwise value is smaller.
Wherein, G (i, j) is the element in gray level co-occurrence matrixes, and L is number of greyscale levels.
4) it repeats the above steps and extracts the feature of all sample images, be set as sample characteristics collection m;
5) image classification model training: the method for using cross validation divides sample characteristics collection m for n subset, at random N-1 subset therein is chosen to train svm classifier model, remaining 1 subset is used to test.It is needed in model training excellent There are two the parameter of change is main, it is penalty factor (C) and nuclear parameter (g) respectively, finds optimized parameter using grid optimizing method.This The kernel function of SVM is Radial basis kernel function (RBF) in embodiment:
Wherein, nuclear parameter g represents the width of kernel function.
The default value of the penalty factor (C) and nuclear parameter (g) is 500 and 0.5.The penalty factor (C) that searches out and The optimal value of nuclear parameter (g) is 7000 and 0.006.
6) apple leaf disease Classification and Identification: optimal svm classifier can be obtained in the optimized parameter found using step 5) Test sample image is input to trained svm classifier model, obtains recognition result as shown in Figure 7 and Figure 8 by model.
See Table 1 for details for predicted time and recognition accuracy of the present embodiment to apple leaf disease recognition, can by data in table 1 To find out that the accuracy rate of the present embodiment has reached 96.67%, while predicted time also can satisfy requirement of real-time.
1 test image recognition result table of table
Parameter value Predicted time Recognition accuracy
Default parameters 3ms 83.33%
Optimized parameter 2.3ms 96.67%
Schematically the present invention and embodiments thereof are described above, description is not limiting, institute in attached drawing What is shown is also one of embodiments of the present invention, and actual structure is not limited to this.So if the common skill of this field Art personnel are enlightened by it, without departing from the spirit of the invention, are not inventively designed and the technical solution Similar frame mode and embodiment, are within the scope of protection of the invention.

Claims (10)

1. a kind of leaf disease recognition method based on machine learning algorithm, which is characterized in that the steps include:
1) collect leaves sample image, collects healthy image and each 100 width of disease geo-radar image altogether, and pre-process to image;
2) pretreated sample image is split using adaptive threshold fuzziness method, the texture of prominent sample image is special Sign;
3) color characteristic of sample image and the textural characteristics of segmented image are extracted based on RGB color;
4) feature for extracting all sample images is set as sample characteristics collection m, constructs svm classifier using the method for cross validation Model;
5) optimized parameter is found using grid optimizing method, the parameter is penalty factor and nuclear parameter g;
6) optimized parameter found using step 5), can be obtained optimal svm classifier model, test sample image is input to Trained svm classifier model, obtains recognition result.
2. a kind of leaf disease recognition method based on machine learning algorithm according to claim 1, it is characterised in that: step It is rapid 1) described in image preprocessing process are as follows: size adjusting and gray processing are carried out to sample image size first and handled, ruler is adjusted Very little is 100*100, then carries out image enhancement operation to gray level image using algorithm of histogram equalization, is finally filtered using intermediate value Wave algorithm carries out denoising to image.
3. a kind of leaf disease recognition method based on machine learning algorithm according to claim 2, it is characterised in that: institute The image enhancement operation process stated are as follows: by the grey scale pixel value Normalized Grey Level value of original image and count each gray-scale point first Then cloth probability convert and be converted according to identical gray level to normalized gray value using accumulation transforming function transformation function For the gray value of standard, to obtain equalisedization treated new images.
4. a kind of leaf disease recognition method based on machine learning algorithm according to claim 1-3, special Sign is: the process that step 2) is split sample image are as follows:
Setting gray value of the image at pixel (a, b) is k (a, b), B × B window centered on pixel (a, b), Middle B indicates the side length of window, and C indicates difference, calculates the threshold value v (a, b) of each pixel (a, b) in image, and calculation formula is as follows:
Binaryzation is carried out point by point with v (a, b) value to pixel (a, b) each in image:
5. a kind of leaf disease recognition method based on machine learning algorithm according to claim 4, it is characterised in that: step It is rapid 3) described in color characteristic include tri- channels RGB six mean value, variance, the degree of bias, kurtosis, entropy and gradient features, texture Feature includes angular second moment ASM, entropy ENT, contrast C ON and unfavourable balance battle array IDM.
6. a kind of leaf disease recognition method based on machine learning algorithm according to claim 5, it is characterised in that: step It is rapid 3) described in color characteristic be expressed as follows:
A. mean value:
B. variance:
C. the degree of bias:
D. kurtosis:
E. entropy:
F. gradient:
In above formula, p (k) indicates the distribution probability that grey scale pixel value is k in the picture, nkFor its frequency, n is sum of all pixels, and L is Number of greyscale levels,For mean value, δ2For variance, kkFor the degree of bias, kFFor kurtosis, kEFor entropy, M × N indicates the size of image,Indicate ash Angle value is the pixel gradient in the horizontal direction of k,Expression gray value is gradient of the pixel of k in vertical direction, kgIt is flat for it Equal gradient.
7. a kind of leaf disease recognition method based on machine learning algorithm according to claim 6, it is characterised in that: step It is rapid 3) described in textural characteristics be expressed as follows:
A. angular second moment:
B. entropy:
C. contrast:
D. unfavourable balance battle array:
Wherein, G (i, j) is the element in gray level co-occurrence matrixes, and L is number of greyscale levels.
8. a kind of leaf disease recognition method based on machine learning algorithm according to claim 7, it is characterised in that: step It is rapid 4) described in svm classifier model kernel function be Radial basis kernel function:
Wherein, nuclear parameter g represents the width of kernel function.
9. a kind of leaf disease recognition method based on machine learning algorithm according to claim 8, it is characterised in that: step It is rapid 5) described in penalty factor and the default value of nuclear parameter g be respectively 500 and 0.5.
10. a kind of leaf disease recognition method based on machine learning algorithm according to claim 8, it is characterised in that: The optimal value of the penalty factor and nuclear parameter g that search out in step 5) is 7000 and 0.006.
CN201811087765.8A 2018-09-18 2018-09-18 A kind of leaf disease recognition method based on machine learning algorithm Pending CN109308697A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811087765.8A CN109308697A (en) 2018-09-18 2018-09-18 A kind of leaf disease recognition method based on machine learning algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811087765.8A CN109308697A (en) 2018-09-18 2018-09-18 A kind of leaf disease recognition method based on machine learning algorithm

Publications (1)

Publication Number Publication Date
CN109308697A true CN109308697A (en) 2019-02-05

Family

ID=65225162

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811087765.8A Pending CN109308697A (en) 2018-09-18 2018-09-18 A kind of leaf disease recognition method based on machine learning algorithm

Country Status (1)

Country Link
CN (1) CN109308697A (en)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110346312A (en) * 2019-07-19 2019-10-18 安徽大学 Winter wheat fringe head blight recognition methods based on Fei Shi linear discriminant and support vector machines technology
CN110378433A (en) * 2019-07-24 2019-10-25 重庆大学 The classifying identification method of bridge cable surface defect based on PSO-SVM
CN110771591A (en) * 2019-11-13 2020-02-11 吉林省农业机械研究院 Medicine device is spouted to automatic variable based on image recognition
CN110874835A (en) * 2019-10-25 2020-03-10 北京农业信息技术研究中心 Crop leaf disease resistance identification method and system, electronic equipment and storage medium
CN112580659A (en) * 2020-11-10 2021-03-30 湘潭大学 Ore identification method based on machine vision
CN113112451A (en) * 2021-03-08 2021-07-13 潍坊科技学院 Green leaf disease characteristic optimization and disease identification method based on image processing
CN113679082A (en) * 2021-06-24 2021-11-23 中国烟草总公司郑州烟草研究院 Intelligent cigar cutting method and device
CN113688959A (en) * 2021-10-26 2021-11-23 寿光得峰生态农业有限公司 Plant disease and insect pest diagnosis method and system based on artificial intelligence

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102214306A (en) * 2011-06-16 2011-10-12 中国农业大学 Leaf disease spot identification method and device
CN103514459A (en) * 2013-10-11 2014-01-15 中国科学院合肥物质科学研究院 Method and system for identifying crop diseases and pests based on Android mobile phone platform

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102214306A (en) * 2011-06-16 2011-10-12 中国农业大学 Leaf disease spot identification method and device
CN103514459A (en) * 2013-10-11 2014-01-15 中国科学院合肥物质科学研究院 Method and system for identifying crop diseases and pests based on Android mobile phone platform

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ON2WAY: "Python下opencv使用笔记(四) (图像的阈值处理)", 《CSDN HTTPS://BLOG.CSDN.NET/ON2WAY/ARTICLE/DETAILS/46812121》 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110346312A (en) * 2019-07-19 2019-10-18 安徽大学 Winter wheat fringe head blight recognition methods based on Fei Shi linear discriminant and support vector machines technology
CN110346312B (en) * 2019-07-19 2021-08-10 安徽大学 Winter wheat head gibberellic disease identification method based on Fisher linear discrimination and support vector machine technology
CN110378433A (en) * 2019-07-24 2019-10-25 重庆大学 The classifying identification method of bridge cable surface defect based on PSO-SVM
CN110874835A (en) * 2019-10-25 2020-03-10 北京农业信息技术研究中心 Crop leaf disease resistance identification method and system, electronic equipment and storage medium
CN110771591A (en) * 2019-11-13 2020-02-11 吉林省农业机械研究院 Medicine device is spouted to automatic variable based on image recognition
CN112580659A (en) * 2020-11-10 2021-03-30 湘潭大学 Ore identification method based on machine vision
CN113112451A (en) * 2021-03-08 2021-07-13 潍坊科技学院 Green leaf disease characteristic optimization and disease identification method based on image processing
CN113679082A (en) * 2021-06-24 2021-11-23 中国烟草总公司郑州烟草研究院 Intelligent cigar cutting method and device
CN113688959A (en) * 2021-10-26 2021-11-23 寿光得峰生态农业有限公司 Plant disease and insect pest diagnosis method and system based on artificial intelligence
CN113688959B (en) * 2021-10-26 2022-02-18 寿光得峰生态农业有限公司 Plant disease and insect pest diagnosis method and system based on artificial intelligence

Similar Documents

Publication Publication Date Title
CN109308697A (en) A kind of leaf disease recognition method based on machine learning algorithm
Gavhale et al. An overview of the research on plant leaves disease detection using image processing techniques
Sarangdhar et al. Machine learning regression technique for cotton leaf disease detection and controlling using IoT
Zhou et al. Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching
CN106525732B (en) Rapid nondestructive detection method for internal and external quality of apple based on hyperspectral imaging technology
CN107316289A (en) Crop field spike of rice dividing method based on deep learning and super-pixel segmentation
CN106845497B (en) Corn early-stage image drought identification method based on multi-feature fusion
CN109101891A (en) A kind of rice pest detection system and its detection method merging artificial intelligence
Pereira et al. Recent advances in image processing techniques for automated harvesting purposes: A review
CN109034269A (en) A kind of bollworm female male imago method of discrimination based on computer vision technique
CN110705655A (en) Tobacco leaf classification method based on coupling of spectrum and machine vision
CN112257702A (en) Crop disease identification method based on incremental learning
Ganatra et al. A survey on diseases detection and classification of agriculture products using image processing and machine learning
Shaikh et al. Citrus leaf unhealthy region detection by using image processing technique
Wang et al. Improved rotation kernel transformation directional feature for recognition of wheat stripe rust and powdery mildew
CN201041547Y (en) Device for analyzing and recognizing different types of green teas based on multi-spectrum image texture
CN109325431A (en) The detection method and its device of vegetation coverage in Crazing in grassland sheep feeding path
Ji et al. Apple color automatic grading method based on machine vision
Ullagaddi et al. Automatic Robust Segmentation Scheme for Pathological Problems in Mango Crop.
CN111860330A (en) Apple leaf disease identification method based on multi-feature fusion and convolutional neural network
CN110827273A (en) Tea disease detection method based on regional convolution neural network
Tumang Pests and Diseases Identification in Mango using MATLAB
Ye et al. Cucumber appearance quality detection under complex background based on image processing
Singh et al. A novel algorithm for segmentation of diseased apple leaf images
CN110376202A (en) Tea tree anthracnose scab recognition methods based on imaging hyperspectral technique

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination