Inception-V3 Versus VGG-16: in Rice Classification Using Multilayer Perceptron

Ichsan Firmansyah (Magister of Computer Science, Potensi Utama University)
Rika Rosnelly (Magister of Computer Science, Potensi Utama University)
Wanayumini Wanayumini (Magister of Computer Science, Potensi Utama University)

Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC)
Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In

Rice is an intriguing research topic, particularly in computer vision fields, because it is a staple food consumed in many parts of the world. Different rice varieties can be classified using the rice grain image based on their textures, sizes, and colors. To extract features from rice images, we used two popular pre-trained convolutional neural network models, Inception V3 and VGG 16. The extracted features are then used as transfer learning in six variations of multilayer perceptron models, using rectified linear units as the activation function and adaptive moments as the loss function. The results show that the VGG 16 network performs better than the Inception V3, with 0.5% higher accuracy, precision, and recall value. Also, using the VGG 16 network produces a lower misclassification percentage, compared to the Inception V3 network, with a difference of 2.6%.