Because the corn vein and noise influence the contour extraction of the maize leaf disease, we put forward a new recognition algorithm based on Curvelet and Shape Context (SC). This method can improve the speed and accuracy of maize leaf disease recognition. Firstly, we use Seeded Regional Growing (SRG) algorithm to segment the maize leaf disease image. Secondly, Curvelet Modulus Correlation (CMC) method is put forward to extract the effective contour of maize leaf disease. Thirdly, we combine CMC with the SC algorithm to obtain the histogram features and then use these features we obtain to calculate the similarities between the template image and the target image. Finally, we adopt
In recent years, maize leaf diseases have a great influence on the yield of maize and quality. Therefore, an important problem caused by them on agriculture shows that it is urgent to protect maize from these diseases and recognize the species of those diseases. There are 6 common maize diseases: Rust spots, Leaf blight, Gray leaf spot, Brown patch,
Maize leaf diseases images.
Leaf blight
Brown spot
Gray leaf spot
Rust spot
Small spot
Huang et al. [
Target texture edge feature can be described more accurately by Curvelet in multiscale and multidirection [
In order to improve the speed of maize leaf disease recognition, we need to transform the color disease image to gray disease image. According to the sensitivity of human eyes to the image, we adopt formula (
This paper adopts the SRG [
According to the relationship between the image pixels, the set
The set
The threshold
Bottom pointer and top pointer of stack are compared, and the seeds at the bottom of stack are selected as the seeds of region growing when bottom pointer is less than top pointer.
The points are added into the seeds
Steps 4 and 5 are repeated until
Maize leaf disease image is shown in Figure
Maize leaf disease original image.
Gray maize leaf disease image.
Segmentation result of maize leaf disease.
Compared with wavelet, target texture feature can be described more accurately by Curvelet in multiscale and multidirection. In order to reduce the interference of the contour extraction of disease image, the CMC method which is used for obtaining the disease contour is presented. The method is based on the amplitude of the target edge that is large at adjoining scales, while the amplitude of the noise edge can attenuate rapidly at adjoining scales. According to the size of the maize leaf disease, we divide the image into 5 scales. The correlated coefficient between the third scale and the second scale is described as
The concrete implementation steps are as follows.
The correlated modulus between the third scale and the second scale is calculated.
Formula (
The following rules are used to detect the edge points at the second scale.
If there are 3 or more than 3 correlation coefficient matrices belonging to the edge, the corresponding matrix is the edge. Otherwise, it is noise.
The following rules are used to detect the edge points of the first scale, the fourth scale, and the fifth scale:
The edge points of each scale are reconstituted to accomplish the edge detection.
Figure
Segmentation result of Gray leaf spot.
Detection result of Figure
The SC method presented by Belongie [
The flow of maize leaf disease recognition based on SC is as follows.
The obtained edge detecting point set
The transformation relation between rectangular coordinates and log-polar is as follows:
The points are calculated in log-polar.
The obtained result in the polar coordinate is transformed into 12 directions, and each direction is 30 degrees.
Figure
Schematic diagram of log-polar coordinate.
The similarity between the template image and the target image is calculated.
The similarity between the point
If
Thus, formula (
If the number of target disease image contours is
Figure
60 characteristic attributes of a feature point.
Figure
Feature points and characteristic attributes.
The Leaf blight and Gray leaf spot segmentation and feature extraction are shown in Figures
Leaf blight and Gray leaf spot segmentation results.
Feature extraction results of Gray leaf spot and Leaf blight disease.
Figure
The final matching results.
From Figure
Experimental environment is as follows: the operating system is windows 8, Pentium (R), quad core, 8 G internal storage, and the software is matlabR2011b. The experimental images are from the maize leaf disease database established by Institute of Crop Science, Chinese Academy of Agricultural Sciences.
744 images are selected to verify the accuracy of the proposed algorithm. Every disease responds to 124 images, and there are 6 kinds of diseases. There are four templates for each type of disease and 120 test samples. Figure
Brown spot samples.
In the experiment, 6-fold cross validation is used to train and test the proposed algorithm. The 744 plant disease images are divided into 6 independent subsets, and each subset contains 124 plant disease images. Each subset has 4 template images and 120 training samples. The training and testing process is repeated 6 times, and finally the 6-time test results are averaged. The recognition rate between the same kinds of diseases and different kinds of diseases is calculated, respectively.
Figure
Experiment results between different kinds of diseases.
Figure
Experiment results between the same diseases.
Figure
Recognition rates between the same diseases.
Figure
Recognition rate among the different classes diseases.
From the above, the results are shown in Table
Maize leaf disease recognition accuracy (%).
Recognition rates between the same diseases | Recognition rate among the different classes diseases | The total recognition rate |
---|---|---|
92.225% | 96.667% | 94.446% |
From the experiment results, recognition rates between the same diseases and recognition rate among the different classes diseases reach above 90%. In the same diseases recognition, the recognition rate of Brown patch is the highest (97.385%), and the Small spot disease has the lowest recognition rate (88.673%). In the different classes diseases recognition, Brown patch has the highest recognition rate (98.658%), and Gray leaf spot has the lowest recognition rate (93.434%). Some edges extracted from Gray leaf spot and
Because the corn vein and noise influence the contour extraction of the maize leaf disease, we put forward a new maize leaf disease recognition algorithm using the Curvelet technique and SC. This method can improve the speed of maize leaf disease recognition. The Curvelet-SC descriptor was put forward based on SC algorithm and the principle of this algorithm was elaborated in the paper. It combined Curvelet and SC for shape matching and texture recognition.
With the experimental analysis, the average accuracy of Curvelet-SC descriptor for maize leaf disease recognition is up to 94.446%. Thus, Curvelet-SC shape descriptor is an efficient method for disease recognition. The proposed algorithm can recognize the maize leaf disease, and it also has guiding significance for other disease recognition to an extent.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors are grateful to the anonymous reviewers who made constructive comments. This work is supported by the National Natural Science Foundation of China (nos. 61203302 and 51107088) and the Tianjin Research Program of Application Foundation and Advanced Technology (no. 14JCYBJC18900).