Automatic segmentation of plant disease images based on graph cuts fusing multiple features

Diseases in crops can lead to declines of production and quality, which cause economic losses in agricultural industry worldwide. Therefore, detection of the diseases in plants is extremely critical for sustainable agriculture. Many crop diseases perform on the leaves, and often present in the form of spots, so processing the leaf images is a feasible way for identifying and diagnosing diseases. Spots separation from the leaf is a very important step in the process of disease recognition and diagnosis. And the segmentation accuracy affects the reliability of the subsequent feature extraction and the accurateness of following classification directly. To improve the segmentation performance, an automatic segmentation algorithm based on graph cut which fused multiple features was put forward in this paper. Firstly the background was excluded by threshold method so as to speed up the image segmentation. The experimental comparison results showed that the segmentation effect for color images processed by OTSU algorithm, a simple adaptive threshold image segmentation method, was not very satisfactory. Therefore, a new method of threshold processing was studied here, which could remove most of the background and did not lose the disease spots. In addition, the red component of the original color images was used as the research objects of the new threshold method, since it had the strongest contrast compared with the green component and the blue component. Then three features, texture, gray level and distance were fused to build the boundary term of the energy function, which described the similarity between the pixels. Among them, for the sake of reducing the computational complexity and calculation time, the texture feature was simply defined by the one-dimensional entropy of images, which was the amount of information included by the gathered characteristics of gray level distribution. Moreover, in order to reflect the extent of the pixel belongs to the background or target, the red component difference between pixels in the image region and the region boundary was used to set up the area term of the energy function automatically. Finally the maximum flow algorithm was utilized to solve the established energy function, and the segmentation results were obtained. With the purpose of verifying the validity of the proposed algorithm, the method was applied to divide three kinds of cucumber disease (target spot, downy mildew and powdery mildew) leaf images. Each disease of 50 pictures, a total of 150 pictures were selected randomly as the experimental samples. And the OTSU algorithm and semi-automatic graph cuts algorithm were chosen as the contrast means. The experimental results demonstrate that the disease spots of the leaf images can be separated effectively when using the method proposed in this paper. The average error rate is 1.81%, which is lower than the other two algorithms, and its average segmentation time do not significantly increase. This study can provide a technical reference for the automatic identification and diagnosis of cucumber diseases in the future.