Optimized Light-Weight Deep Learning Model for Rice Disease Identification
Keywords:
Data augmentation, pruning, deep learning, compression, fine-tuningAbstract
From past decade, deep learning models have gained the Convolutional neural network models have made substantial progression in the agricultural sector. But the utilization of the deep learning models is restricted confined because of enormous supernumerary and imperative parameters. In this article , the magnitude based pruning and dynamic range quantization have been employed to optimize the CNN model so as to be deployed on edge devices for the identification of four classes of rice leave i.e. brown spot, hispa, leaf blast and healthy. Experimental results show that classification accuracy achieved by baseline CNN Model for brownspot -97.15%, hispa- 97.03%, leaf blast- 96.94% and the healthy-96.9%.Overall test accuracy using baseline CNN model is 98.11%, using magnitude base pruning is 97.39% and using dynamic range quantization and pruning is 96.02%.The initial model size of the cnn model without pruning is 78.24 MB, model size with pruning is 25.743 MB, and with quantization model size achieved is 21.88 MB. The proposed work can deploy the models on edge devices that would be light weight with less memory consumption.
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