Abstract: Abstract: Image detection of crops' disease spots can be helpful for real-time monitoring and diagnosis of diseases and pests. Plant Disease Images were diverse and complex. Many methods have been studied to detect and recognize disease spots. In order to realize real-time and accurate detection, the detection algorithm of disease spots must be both steady and simplified. This paper proposed a rapid detection method of disease spots based on edge detection and improved Hough transform. Edge detection needed a gray image. Disease images were calculated into feature images by R, G, B, or difference components, according to disease spots in the images. The edges were first detected by a Canny operator. A mended template was designed to repair the broken edge into the close edge. The close edge was picked by morphological operation. The close edges contained disease spots, also other background edges. Farther filtration was needed to obtain the close edge of disease spots. Edge detection and repair was the base for the Hough transform. As the disease spots grew and developed approximately circular, round Hough transform was improved to detect the edge of the disease spot. Direct application of Hough transform to the disease spots edges would result in great calculation and inaccuracy fitting, as there might be a large gap between the radiuses of the disease spots. The edges of disease spots were coded and divided into quasi-circular disease spots and irregular disease spots, according to the ratio between the long radius and the short radius of each spot. The quasi-circular disease spots were fitted by a single circle, the irregular disease spots were fitted by multiple circles. The transform parameters of each edge were determined according to the disease spots edge. Hough transform were carried out on each edge in independent space. So that error accumulation could be avoided. The largest intersected Hough circle would be kept. The edge of the disease spot was fitted by the circular and the center of the disease spot so it could be located. 90 crop diseases images were used to detect disease spots. These images which included leaf spots and fruit spots were randomly pictured in the field. The detection algorithm was programmed to carry out the detection. The results showed that the detected circles covered most of the disease spots; centers of the disease spots were also well located. Statistics results showed that the circle fitting accuracy was 87.01%, the central error was 4.44%. The running times of the Hough transform for each image were less than 5 s. The results demonstrated that this detection algorithm could detect disease spots both quickly and accurately. Meanwhile, the edges of the disease spots could be effectively identified and better recognized.