Rice leaves disease classification using deep convolutional neural network

Vidyadevi G. Biradar; H. Sarojadevi; J Shalini; R. S. Veena; V Prashanth

doi:10.53730/ijhs.v6nS4.6067

Authors

Vidyadevi G. Biradar Nitte Meenakshi Institute of Technology
H. Sarojadevi Nitte Meenakshi Institute of Technology
Shalini J RV Institute of Technology and Management
Veena R. S. RV Institute of Technology and Management
Prashanth V Nitte Meenakshi Institute of Technology

Keywords:

CNN, VGG 16, inception V3, rice blast

Abstract

The rice disease due to fungus, bacteria, spot and sheath blight, leaf scald effects the crops yield. The farmers have limitation predicting the quality on the crop for large scale evaluation. Therefore, there is a need for an automatic leaves disease prediction tool to assists to apply corrective procedures. Deep learning models have outperformed in several sectors of computer vision. In this paper a deep leaning model based on pre-trained CNN is customized through altering the architecture of the models and apply transfer learning methods and the resulting model named PaddyLeaf15 CNN is evaluated on the benchmark dataset from Kaggle. The results indicate that the proposed model outperforms as compared to VGG-16 and Inception V3 based models with highest model accuracy of 95%.

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References

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