Site-specific variable pesticide application is one of the major precision crop production management operations. Rice blast is a severe threat for rice production. Traditional chemistry methods can do the accurate crop disease identification, however they are time-consuming, require being executed by professionals and are of high cost. Crop disease identification and classification by human sight need special crop protection knowledge, and is low efficient. To obtain fast, reliable, accurate rice blast disease information is essential for achieving effective site-specific pesticide applications and crop management. The present paper describes a multi-spectral leaf blast identification and classification image sensor, which uses three channels of crop leaf and canopy images. The objective of this work was to develop and evaluate an algorithm under simplified lighting conditions for identifying damaged rice plants by the leaf blast using digital color images. Based on the results obtained from this study, the seed blast identification accuracy can be achieved at 95%, and the leaf blast identification accuracy can be achieved at 90% during the rice growing season. Thus it can be concluded that multi-spectral camera can provide sufficient information to perform reasonable rice leaf blast estimation.