Abstract
Rice diseases is one of the recurring factors of failed harvest and reduced rice production output and it is one of the biggest problems for Indonesia especially because of Indonesia’s population dependent of rice as it’s staple food. Pest control using fast dan early detection is one of the solutions to reduce the potential of failed harvest. Using a pretrained model of MobileNetV2, a model has been successfully built to detect for different types of rice diseases using convolutional neural network architecture and transfer learning method. The modelling process uses 7.077 image sample of rice infected by bacterial blight, blast, brown spot, and tungro. The model then can be accessed through website and has 99% of classification accuracy. Furthermore, using Gradient-Weighted Class Activation Mapping (Grad-CAM) rice disease can then be marked on top of the original image as a heat map, providing the users easier way to localize dan verify the classification result faster.
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