IndoHerb: Indonesia Medicinal Plants Recognition using Transfer Learning
  and Deep Learning
Muhammad Salman Ikrar Musyaffa Novanto Yudistira Muhammad Arif Rahman
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