Abstract: Abstract: Leaf is the important part of a plant, and leaf vein and edge feature are often used for classifying the plant. Leaf vein and edge features can also indicate the growing condition of plant. Leaf vein and edge extraction is useful for studying leaf and plant structures. However, it is difficult to obtain the accurate leaf vein and edge because of uncertainties in the process of image acquisition and processing. So the extraction algorithm of leaf vein and edge is required. The traditional algorithms can detect leaf edge and vein, but the interference immunity is poor and is easily affected by noise. So the edge is not complete and it is difficult to detect the complicate edge and small vein. The tradition algorithm is not adequate to feature extraction of plant leaf in complicated conditions. Recently, the new extraction methods are emerging, including neural network, fuzzy theory, and morphology, etc. However, each algorithm has its own limitations the result of extraction is not ideal. In this paper, plant leaf vein and edge extraction based on fuzzy order morphology was proposed. The proposed method combined the fuzzy theory and order morphology to extract the leaf vein and edge. Firstly, in this study, we constructed membership function according to the pixel neighborhood characteristic, which was based on the difference between the leaf vein edge and inner filed. The leaf image was transformed from the spatial domain to the fuzzy domain. The value of membership reflected the subjection of pixel to edge or vein. We also made the curve of membership function, which intuitively showed the distribution of pixel to edge or vein. Secondly, fuzzy rule and fuzzy inference needed to be proposed. The good rule and inference could obtain a good enhancement. We defined the fuzzy rule according to Sugeno fuzzy model, which could increase the difference of edge and inner area. If the value of membership was high, the value was higher by fuzzy inference and vice versa. We chose the power function as the fuzzy rule. When the x(x was the value of membership and value range was 0 to 1) was vein or edge, the exponent value was far less than 1. When x was in inner area, the value of x could keep up, so the exponent value was 1.Next, we extracted the vein and edge using the order morphology. In order morphology, when structure element was in flat area, the output image was almost same to the input; but when structure element was in changing area, the output image fell or rose on the vein or edge, which made a large difference between the input and the output image. By use of those, the vein and edge of leaf can be extracted. After enhancement, the plant leaf's vein and edge was more obvious. The order morphological transformation could make the vein and edge area gray scale more obvious. We constructed three edge detection operators, which could choose different structure element and different percent. The vein and edge of leaf could be obtained using the three operators. At last, we chose the leaf in natural situation and made simulation using MATLAB 7.0. To verify the effectiveness of the algorithm, we conducted several groups of experiments. The results showed that the proposed algorithm this paper could extract the vein and edge accurately. Comparing to several tradition algorithm, the edge was clearer and pseudo edge was disappear, and the noise could also be suppressed. The fuzzy edge and small vein could be detected after fuzzy transform. The proposed algorithm in this paper took into account of the difference between edge pixel and inner pixel in defining the membership function. The difference was further increased by fuzzy inference using fuzzy rule. So the algorithm could detect much clear and accurate leaf vein and edge, which has wide application. The method also can provide tools for classifying plant and monitoring the growing condition.