Rice Diseases Recognition Using Transfer Learning from Pre-trained CNN Model - Hamhongsa, Wittawat; Wiangsripanawan, Rungrat; Thorncharoensri, Pairat (My University, 2023)
This research aims at applying the well-known pre-trained convolutional neural network (CNN) image classification algorithms such as InceptionV3, Xception, ResNetV2, InceptionResNetV2, and DenseNet to classify the five diseases of rice's leaves in Thailand. Data sets used are from 3 sources: UCI database, Rice Leaf Disease Image Samples dataset and images collected by authors in Thailand between 2018–2020. Our initial experimental result shows that using the pre-trained CNN models to classify the rice leaves disease seems to be possible with a greater number of images required. Therefore, the image data augmentation technique is used to add the number of images into the dataset. The experimental result with data augmentation shows that it could increase rice disease classification efficiency up to 16.533% (especially for the ResNet50V2 model).
Keywords: Convolutional neural network; Deep learning; Pre-trained CNN model; Rice diseases; Transfer learning