1Department of Computer Science, Faculty of Mathematics and Natural Sciences, Pakuan University, Indonesia, Universitas Pakuan, Indonesia
2Jl. Pakuan RT. 02 RW. 06 Tegallega Kecamatan Bogor Tengah Kota Bogor 16129, Indonesia
BibTex Citation Data :
@article{JTSISKOM14571, author = {Fahmi Fiqri and Sri Setyaningsih and Asep Saepulrohman and Agus Ismangil}, title = {Rice Disease Image Classification using MobileNetV2 Pretrained Model with AcivationAttention Visualization using Gradient-weighted Class Activation Mapping (Grad-CAM)}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {11}, number = {1}, year = {2024}, keywords = {disease; rice; transfer learning; convolutional neural network; grad-cam}, 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. }, issn = {2338-0403}, doi = {10.14710/jtsiskom.2023.14571}, url = {https://jtsiskom.undip.ac.id/article/view/14571} }
Refworks Citation Data :
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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