Abstract: The shape parameter of wheat spike is a direct reflection of the wheat growth status. And it is also an important parameter which the species breeding and test experts care about. In order to achieve the non-destructive measurement of wheat spike morphological characteristics and rapid species classification based on these characteristics, the article proposed the spike traits extraction methods based on morphology: the awn number, the average awn length, the spike length. First, the wheat awn was removed through the wheat image morphological operations so as to get the main image of the wheat. And then calculated the spike length through the method of looking for spindle direction angle and rotating calculation external rectangle length, calculated the awn length and the number of the awn through the method of thinning the wheat awn image and corner detection, and estimated the spike type through width coefficient proportion. Secondly, a three layer BP neural network was designed with eight of the extracted characteristic parameters so as to classify the 240 pictures of 4 wheat varieties. The recognition accuracy rate was 88%. The method can be a reference for rapid species classification of wheat. Taking other external shape characteristics of wheat as supplement input parameters will improve the recognition accuracy greatly.