Abstract:
In Ethiopia there are a lot of medicinal plants that can cure different types of diseases using
their different parts. These plants are often missed by modern medicinal science; they are
mainly known by the people who are an experts on indigenous medicine. This study proposes
a fine-tuned model to classify the medicinal plant parts. The fine-tuning technique on Mobile
Net, VGG16, and InceptionV3 are applied to extract plant features and classify the medicinal
part. The batch size, learning rate, and optimizers are tuned to make the models achieve high
efficiency during prediction of medicinal plant parts. For classification task, Softmax function
is used at the last layer of the CNN. Metrics such as, precision, recall, and F1-Score are used
to evaluate the models. A high-resolution camera for data acquisition and google Colab for
training and testing are used. When analyzing the experimental result, Mobile Net perform
better with an accuracy of 99.84% for training sets and 99.44% for testing sets using learn ing rate of 1e-4, optimizer of Adamax, and a batch size of 32. VGG16 performs 99.78% for
training sets and 99.37% for testing sets using a learning rate of 1e-4, Adamax and batch size
of 128. InceptionV3 performs 96.12% for training sets and 90.53% for testing sets. While
evaluating models using F1_score metric, Mobile Net obtain appreciated performance by scor ing 99.44% using optimizer of Adamx and batch size of 32. Without batch normalization at
fully connected layer, Mobile Net scores 99.27% using Adamax. Generally Mobile Net gain
the best performance using a learning rate of 1e-4, epoch of 30, batch size of 32,and optimizer
Adamax. In this study, Mobile Net is confirmed as the fastest model to train, obtained higher
performance, and is suitable to classify the medicinal plant part. This is not due to the small
number of convolutional layer rather Mobile Net use depthwise separable convolutional layer
to decrease computational complexity (reduce the depth of output feature map by decreasing
scalar multiplication through convolution
