Segmentation algorithm with threshold for wheat lesion based on improved fuzzy edge detection

Abstract: Wheat is an economic crop correlated with national lifeblood, and its yield has a direct impact on people's living standard and economic development, while the occurrence of disease is an important cause of crop yield decline. There are many kinds of crop diseases. Timely detection of disease types and corresponding prevention and control are urgent requirements to reduce the risk of crop yield decline. The disease segmentation is the priority among priorities of disease detection, and segmentation of lesion information is a prerequisite for disease identification, discrimination of disease degree, and pesticide application decision. The picture of wheat taken under natural conditions is greatly affected by the environment. The main obstacle of image segmentation is to find interesting parts in complex background. At present, the RGB (red, green, blue) sub region component segmentation method is usually used for image segmentation, and then the results are obtained by using some merging methods, but there is a large amount of computation in the segmentation of sub region components. For the wheat lesion segmentation, there exist the problems of noise and lesion edge being not clear. The research on wheat lesion image segmentation algorithm shows that the general image segmentation method has poor adaptability and compatibility, and other methods of mixing is difficult to achieve the desired results. Fuzzy edge detection with strong adaptability is the first algorithm to solve such problems. The traditional fuzzy edge detection method is first-order differentiating the preprocessed images, and edge detection is realized by edge discontinuity. Aiming at the disadvantages of the traditional algorithm such as high error rate, easy to lose the weak edge information, an improved image threshold segmentation algorithm based on fuzzy edge detection is proposed in this paper. In the aspect of image preprocessing, after analyzing the shortcomings of the traditional fuzzy edge detection, 2 improvements have been made to the algorithm. Gradient inverse weighted average filtering method is modified for the removal of noise and lesion of wheat, and numerical hierarchical improvement is made to multi-level fuzzy algorithm to enhance the edge information of the lesion. In the threshold segmentation algorithm, parameters directly influence the efficiency of image segmentation, so the level of detail segmentation on wheat spot shape can rely on the regulation of 2 aspects: One is the threshold, and the threshold value is influenced by relative pixel gray difference control; the other is the data involved in the calculation, and the data are related to the time of calculation. Reducing the participation data is the main method to improve the efficiency of the segmentation algorithm. An improved threshold segmentation method for maximum inter-class variance ratio is proposed. Based on the enhancement of image edge, threshold segmentation is applied to improve the threshold selection method. We use improved new formula to classify 2 kinds of variances and improve the overall performance of threshold segmentation from 2 aspects. The traditional threshold segmentation algorithm is improved, which is used to extract wheat spot shape feature from the wheat spot image. Compared with the traditional threshold segmentation algorithm, the improved algorithm based on fuzzy edge enhancement and threshold segmentation achieves an average accurate segmentation rate of 98.76%. The improved algorithm highlights the lesion edge information, and has the advantages of high segmentation efficiency and low noise. The noise ratio is reduced by 8.36 percentage points, and the time consuming is reduced by 0.331 s, which provides a reference for the improvement of image segmentation method. In the process of image segmentation, the improved algorithm is used to segment the wheat disease images, and the average multiple segmentation results are used as the parameters of final segmentation result. From the comparison results of 3 wheat lesion segmentation pictures, it can be seen that the improved algorithm is more meticulous to reflect the morphological characteristics of wheat disease, while retaining the edge information of wheat disease.