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In this study, an automated panicle blast grading methodology using support vector machine based on deep features of small convolutional neural network (CNN) is suggested. MobileNetV2 and ShuffleNet are the two small and powerful architecture among CNN. The transfer learning and deep learning approach are adapted for MobileNetV2 and ShuffleNet for classifying the panicle into three levels; healthy, less blast and high blast. The experimental results reveal the support vector machine (SVM) using deep features perform better compared to its counterpart i.e., transfer learning approach. In addition, the comparative analysis based on the accuracy score of MobileNetV2 & ShuffleNet in transfer learning & deep learning approach with the traditional image classification methods for panicle blast grading is performed. In over all, the ShuffleNet plus SVM is the best classification model for grading of panicle blast with accuracy of 89.37%, sensitivity 89.37%, specificity 94.68% and computational time 14.936 second. [ABSTRACT FROM AUTHOR]