Abstract: Abstract: Damaged leaf is one of the important factors leading to crop loss. Damaged leaf segmentation provides an important basis for diseased leaf detection, and for proper preventive measures to be taken. Advances in technology have made it possible for a computer with image processing techniques to segment the diseased leaf in an image of a green plant and evaluate the severity of the infestation. The research objects based on image segmentation and processing are the leaves damaged by pests or nutrient deficiency. The procedure of image segmentation algorithm was developed in C++ that targets a diseased green leaf including the normal leaf and diseased regions. In current researches, algorithms based on thresholding or clustering are widely used. Despite of the simplicity and efficiency, the performances of these methods are not satisfactory due to the grayscale overlapping among background, plant leaves and damaged leaves in field environment. In consideration of the stability edge feature of images and the gray value consistency of leaves, a novel method was proposed to segment the damaged leaves in field environment by combining Canny edge detection and block mark, which is robust with respect to the changes in illumination and noises, and efficient to evaluate the damage degree of the leaves. Image processing was used to transform the image to gray scale, extract the Canny edge, perform Canny edge clustering, remove noise, detect the external rectangle, extract connected components which are 4-connected, classify regions, and finally segment the diseased regions of the green leaf. The block mark based algorithm was introduced to segment the damage leaf. The experiments were conducted on Malabar spinach, tomato, cucumber, eggplant, peach, pepper, dolichos lablab images captured on sunny day, and towel gourd, calabash, melon, eggplant and cucumber images captured on cloudy day. (1) The classification accuracy of the Malabar spinach on a sunny day was 98.8%; and 95.4%, 98.5%, 98.4%, 98.8%, 99.1%, 99.5% for tomato, cucumber, towel gourd, peach, pepper, dolichos lablab,,respectively, and the average classification accuracy for the test images was 98.4%. The classification accuracy of the towel gourd on a cloudy day was 96.5%, and 97.1%, 95.6%, 96.4%, 88.5% for calabash, cucumber, melon, eggplant, respectively, and the average for the test images was 96.5%. (2) The classification false rate of the test images captured on a sunny day for the Malabar spinach was 0.3%, and 1.2%, 0.2%, 1.2%, 0.1%, 0.0%, 0.1% for tomato, cucumber, towel gourd, peach, pepper, dolichos lablab, respectively, and the average for the test images was 0.3%. The classification false rate on a cloudy day for the towel gourd was 0.1%, and 0.0%, 0.5%, 0.1%, 0.2% for calabash, cucumber, melon, eggplant, respectively, and the average for the test images was 0.2%. (3) The average false rates on the sunny sets and the cloudy sets for leaves damage degree were 2.340% and 1.475%, respectively. Experimental results showed that the proposed method could effectively separate damaged leaves apart from background. The method provided higher precision as well as the accurate and closed boundaries, which was beneficial to evaluate the damage degree of leaves.