IndoHerb: Indonesia Medicinal Plants Recognition using Transfer Learning and Deep Learning

Herbal plants are nutritious plants that can be used as an alternative to traditional disease healing. In Indonesia there are various types of herbal plants. But with the development of the times, the existence of herbal plants as traditional medicines began to be forgotten so that not everyone could recognize them. Having the ability to identify herbal plants can have many positive impacts. However, there is a problem where identifying plants can take a long time because it requires in-depth knowledge and careful examination of plant criteria. So that the application of computer vision can help identify herbal plants. Previously, research had been conducted on the introduction of herbal plants from Vietnam using several algorithms, but from these research the accuracy was not high enough. Therefore, this study intends to implement transfer learning from the Convolutional Neural Network (CNN) algorithm to classify types of herbal plants from Indonesia. This research was conducted by collecting image data of herbal plants from Indonesia independently through the Google Images search engine. After that, it will go through the data preprocessing, classification using the transfer learning method from CNN, and analysis will be carried out. The CNN transfer learning models used are ResNet34, DenseNet121, and VGG11_bn. Based on the test results of the three models, it was found that DenseNet121 was the model with the highest accuracy, which was 87.4%. In addition, testing was also carried out using the scratch model and obtained an accuracy of 43.53%. The Hyperparameter configuration used in this test is the ExponentialLR scheduler with a gamma value of 0.9; learning rate 0.001; Cross Entropy Loss function; Adam optimizer; and the number of epochs is 50. Indonesia Medicinal Plant Dataset can be accessed at the following link https://github.com/Salmanim20/indo_medicinal_plant