Optimasi Convolutional Neural Network NASNetLarge Menggunakan Augmentasi Data untuk Klasifikasi Citra Penyakit Daun Padi

 Afiana Nabilla Zulfa (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 (*)Jasril Jasril Mail (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Muhammad Irsyad (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Febi Yanto (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Suwanto Sanjaya (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: April 5, 2023; Published: April 27, 2023

Abstract

Diseases that attack rice are one of the elements that can reduce rice production. Rice diseases include Blast, Brown Spot, Leaf Smut, and so on. Distinguishing rice disease from sight has a weakness because rice disease has similar symptoms and characteristics. Farmers lack knowledge in identifying rice disease types so that technology is needed that can help distinguish rice diseases. The method used for rice image classification in this study is the Convolutional Neural Network NASNetLarge architecture. There are two classification processes, namely the classification process using data augmentation and without data augmentation. The data consists of 4 classes, namely Healthy, Leaf Smut, Blast, and Brown Spot with a total of 440 original images and 1320 augmented images. This study uses data augmentation, namely Horizontal Flips, Vertical Flips, and Contrast. The results for the classification process without data augmentation obtained the highest accuracy, namely 94.31%, 100% precision, 100% recall, and 100% f1-score at a ratio of 80:20, learning rate 0.1, dense 256, batch size 32, and optimizer Adam. While the accuracy obtained in the classification process using data augmentation is 98.73%, 96.11% precision, 100% recall, and 98.01% f1-score at a ratio of 70:30, learning rate 0.1, dense 16, batch size 128, and the Adagrad optimizer. The accuracy results show that the data augmentation and hyperparameters used can increase the accuracy in classifying rice leaf disease images.

Keywords


Augmentation; Classification; CNN; Rice; NASNetLarge

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