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A Novel Method of Rice Disease Detection Based on Deep Learning

Reenu Susan Joseph, Nisha C.A., Asha Vijayan

Abstract


India's main food crop is rice. Agriculture accounts for 70% of the Indian economy, yet pests and illnesses cause 37% of the production of rice to be lost. The early detection of rice diseases will prevent huge economic loss for the farmer and the proper care can assist farmers to protect their rice crops from various crop diseases. Manual illness diagnosis is a time-consuming and complex technique. Deep learning techniques are most promising one for identification. Therefore, a proper method for detecting common rice diseases (rice blast, bacterial blight, sheath blight, brown spot, & Tungro) based on deep learning is proposed. An experimental study was conducted to identify the best model from VGG16 and ResNet18 for the rice disease detection as well as studied the augmentation effect on models

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References


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