The Edge Detection Of Weeds Images Based On The Wavelet Transform

Herbicide as a kind of effective method widely used weed control, has reduced the amount of manual labor, however it also have brought a series of side effects such as the increasing production costs, crop quality problems and ecological environmental pollution issues. This paper tries to use the computer image processing technology and wavelet analysis to detect edge of the comman weeds in wheat fields. it will lay the foundation of the realization of weed identification.In view of the random perturbation such as containing a certain amount of noise in the image, in order to improve recognition results. Firstly, the true color image obtained by the camera should be preliminary pretreated into the gray. Then we use Median Filter to filt Gaussian white noise. To separate the crop plants, weeds and soil backgrounds, we divide the gray level by the threshold segmentation. To reduce the calculation amount of this paper, we will turn the weeds image into the binary image containing target and a small amount of high-frequency noise.In this paper, the advantages and disadvantages of several classic edge detection methods are firstly discussed and compared during the process of achieving Edge Extraction of weeds image. Wavelet transform is proposed to detect the image because of its "adaptability" and "mathematical microscope nature". The first algorithm is based on the number of B-spline function analysis and theory of mathematical morphology. Take the 2-order B-spline wavelet as wavelet mother function. To avoid removing the weaker edge during the process of Wavelet transform two-dimensional images of weeds based on the Mallat fast algorithm, this paper presents a kind of edge detection of integration of B-spline wavelet edge detection algorithms and mathematical morphology edge detection algorithm, then finally, get the ideal edge synthesized all standard features. The second algorithm is wavelet analysis modulus maxima edge detection method. In according to the relation of Lee index and wavelet transform, we utilize the transmission characteristics of wavelet maxima in different scales to detect out images maximum in the horizontal and vertical direction, then we construct the membership function with fuzzy algorithm to extract weak Edge Information, and finally we get the edge images of different scales. Simulation results show that this method can detect weak Edge. The third algorithm is based on wavelet packet edge detection. The wavelet packet theory is better than the other wavelet analysis. It is not only contained the low-frequency image decomposition but also contained the high-frequency images decomposition, through which way we can get more information. The results have showed that the reconstructed similar partial image can remove the high-frequency components and can be used to detect the edge which can not be detected on the verge of the original image after the wavelet packet is decomposed.In this paper we will make a comprehensive research, contrast and improvement of algorithm for edge detection, which will provide the theoretical support for variable herbicide spraying. The results in this research is of great practical value and of great importance to the agricultural development.