Klasifikasi Penyakit Pada Tanaman Pada Menggunakan Transfer Learning Mobilenetv2 Terbantu Gradient-Weighted Class Activation Mapping (Grad-CAM)
Kode Repository :SKI08/FAH/22
NPM :065118116
Nama :Fahmi Noor Fiqri
Pembimbing 1 :-Dr. Sri Setyaningsih,Dra.,M.Si.
Pembimbing 2 :-Asep Saepulrohman, M.Si
Abstrak :-Abstract – In this research, we proposed an image
classification model based on the MobileNetV2
pretrained model combined with a visual explanation
based on the gradient-weighted class activation (Grad-
CAM) algorithm to build a robust and accurate
classification of rice diseases. The model is based on
convolutional neural network (CNN) architecture.
First, transfer learning is done from the MobileNetV2
pretrained model to create the classification model,
followed by Grad-CAM to produce the visual
explanation of the CNN. Finally, the model is trained
on 7,077 rice images containing four different diseases
(bacterial blight, blast, brown spot, and tungro) with a
data augmentation process to increase the dataset’s
overall variance. This process yields a model with a
classification accuracy of up to 99,9%, combined with
visual feature explanation making this model a robust
and efficient classification model.
Keywords – disease, rice, transfer learning,
convolutional neural network, grad-ca