Abstract: Abstract: Lean meat percentage (LMP) is an important indicator of pig quality, playing an important role in pig breeding and sale. At present, methods for detection of LMP are mostly destructive, i.e., by way of segmentation, weighing and calculation. However, advanced ultrasound equipment is expensive, and most individual farmers are unable to afford the cost. For slaughtering and food processing industries, it is very necessary to develop a rapid nondestructive LMP detection method. In this study, machine vision technology was applied to estimate LMP through external physical characteristics of pigs, so as to provide decision-making basis of pigs' quality for breeders and buyers. Therefore, technology should have a capacity of processing a large amount of vision information and high detection speed, and use a nondestructive detection method capable of acquiring global indexes. With MATLAB as a development tool, in this study, we realized the software interface through the Graphical User Interface (GUI), and selected the side image and back image of pigs as research objects. Different focal lengths and object distances would result in different ratio scales of images. To avoid these factors, ratios of parameters were selected rather than specific length, area and so on. Firstly, 116 sets of measured data were collected and analyzed. The results showed that the ratio of chest depth to body height, the ratio of hip width to body length, the ratio of hip width to waist width and the ratio of abdomen length to body length had certain relations with the LMP. Secondly, with 100 sets of measured data as training samples and remaining 16 sets of measured data as test samples, a prediction model based on radial basis function(RBF) neural network was built. The results showed that the average error of the test samples was 0.31%, and the maximum and minimum errors were respectively 0.47% and 0.07%. The precision and rate of the network all fulfilled the requirement. Then, seven groups of pig images were photographed, and after image gray processing and preprocessing by a series of weighted formulas, binaryzation by Otsu method, and secondary image denoising by morphological operations, and outline shapes were extracted. Based on Harris algorithm and inherent external physical characteristics of living pigs, we extract body length, body height, chest depth, abdomen length, hip width, waist width and other characteristic parameters. Finally, the calculated parameters were used as the input in the model to obtain corresponding LMP values which were compared with the measured data to verify feasibility of the method. In this study, seven groups of pig shape images were processed, respectively. The average estimated accuracy rates of the four ratios were 92.90%, 92.44 %, 95.17% and 96.51%, respectively. The average estimated accuracy rate of LMP reached 94.35%, and the maximum and minimum errors were 6.56% and 3.57%, respectively. The results showed that the new assessment method based on shape characteristics could be used for estimation of LMP of pigs with low cost and high efficiency. Furthermore, the future development trends of machine vision on nondestructive test of livestock were proposed since it prevents from the animal stress and anthropozoonosis.