Abstract: Site-specific variable pesticide application is one of the major precision crop production management operations. Barley scab identification and classification by human sight need special crop protect knowledge with the lower efficiency. A method for real-time and reliable detection of barley disease was developed. The samples of the infected barley and the healthy barley were collected. The backgrounds of images were removed by using the Nir channel image and the threshold segmentation algorithm, and the barley awn was removed by imopen function. Then statistical characteristics of images were captured, including the mean values and variances of the gray values of the image. After the statistical characteristics were preprocessed, Partial Least Squares (PLS) analysis was applied as calibration method as well as a way to extract the new eigenvectors which could be used to represent the information of original image data. The selected new eigenvectors were used as the input data matrix of least squares-support vector machine (LS-SVM) to develop LS-SVM identifying models and the barley, which was infected by barley scab or not, were used to be the outputs of the LS-SVM model. It was found that the the LS-SVM model was the best method with the predicting accuracy of 93.9%. The results indicateed that the method of identifying barley scab based on multi-spectral images was feasible. Thus, it is concluded that multi-spectral imaging technique is available for the detection of barley scab on the barley spike.