Microscopic Image Acquisition System for Vehicle-Spraying Spore Catcher

The microscopic image acquisition system of the car-spore-harvesting trap is a valuable function. It is very effective for the collection of spores of diseased poplars. It is then studied using digital image processing and recognition technology. Finally, spores are automatically identified and counted. This method not only improves the accuracy of counting and the speed of data collection, but also saves a lot of manpower and material resources, deservedly and quickly advanced technological means.

The background light is removed and the image is grayed out. In order to eliminate the influence of light source intensity change and noise interference in the system, the original image is subtracted from the background light image, and the total brightness formula is used to convert the color image into an 8-bit grayscale image: f =0222R+0.7076+0.071B where f is the brightness of the grayscale image after conversion, that is, the grayscale value.

Car drive spore catcher IMHS smoothing algorithm, in order to improve the spore recognition rate, must further remove the noise in the image. Based on the research and comparison of existing smoothing techniques, this paper proposes an improved maximum homogeneity smoothing algorithm ((1) (x,y) point repetitive pair ((x,y) for image f(x,y). The 17 trapezoidal areas of ) are evaluated for range and median values, the minimum range is determined, and the median is assigned to points ((X, Y).

In general, when the structural element is eight-connected, the resulting boundary is four-connected; when the structural element is four-connected, the resulting boundary is eight-connected. The spore boundary was extracted to calculate the circumference and to obtain the Fourier leaf, and the eight-connected boundary was smooth and closer to the real manna. Therefore, this paper uses four connected structural elements. Due to the breadth of the breadth in the picture, it must be refined. This article uses a combination of string and string processing methods, which is based on the Hilditch classic refinement algorithm, to make some improvements.

The spore images were obtained from the microscopic image acquisition system of the spore capture device of the vehicle. After removing noise, smoothing the image, segmenting and dilating the threshold, a binarized image was obtained, and then edge extraction and refinement were performed. Finally, spores were automatically realized. count. In the study, 53 spore images of poplar diseases were automatically identified and counted, and the accuracy rate reached 9800. This technology provides a rapid and advanced method for predicting and forecasting poplar disease.

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