Classification of Ornamental Plants with Convolutional Neural Networks and MobileNetV2 Approach

Abstract

Indonesia has two seasons, and the potential as a producer of superior products in the plantation sector is tremendous. Coverage in the plantation sector has ornamental plant species. Ornamental plants are plants that can be used as decorations indoors or outdoors. Each form of the plant is diverse and has its charm. Some Indonesian people still do not know the types of ornamental plants, so one of the efforts is to introduce ornamental plants to the public. In this case, with conditions that are currently digital, computer applications can be used to introduce ornamental plants. Therefore, there is a technology with the Deep Learning method using Convolutional Neural Networks. Using the dataset obtained, there are 1554 images with five categories of ornamental plants divided by a ratio of 80% train data and 20% test data. Then using the Pareto principle, the train data will be divided into 80% train data and 20% data validation. After the training and testing, the accuracy results are 75% for train data and 67% for data validation. Several experiments were conducted to find the parameters that get the model with the best accuracy, namely by experimenting with the MobilenetV2 model.