Image classification of lotus in Nong Han Chaloem Phrakiat Lotus Park using convolutional neural networks

Abstract

The Nong Han Chaloem Phrakiat Lotus Park is a tourist attraction and a source of learning regarding lotus plants. However, as a training area, it lacks appeal and learning motivation due to its conventional presentation of information regarding lotus plants. The current study introduced the concept of smart learning in this setting to increase interest and motivation for learning. Convolutional neural networks (CNNs) were used for the classification of lotus plant species, for use in the development of a mobile application to display details about each species. The scope of the study was to classify 11 species of lotus plants using the proposed CNN model based on different techniques (augmentation, dropout, and L2) and hyper parameters (dropout and epoch number). The expected outcome was to obtain a high-performance CNN model with reduced total parameters compared to using three different pre-trained CNN models (Inception V3, VGG16, and VGG19) as benchmarks. The performance of the model was presented in terms of accuracy, F1-score, precision, and recall values. The results showed that the CNN model with the augmentation, dropout, and L2 techniques at a dropout value of 0.4 and an epoch number of 30 provided the highest testing accuracy of 0.9954. The best proposed model was more accurate than the pre-trained CNN models, especially compared to Inception V3. In addition, the number of total parameters was reduced by approximately 1.80–2.19 times. These findings demonstrated that the proposed model with a small number of total parameters had a satisfactory degree of classification accuracy.