Abstract:
"Ayurveda plants are being used for medical treatments all over the world, these treatment
concepts have been there for many years back. Ayurveda treatments are non-toxic and contains
fewer side effects, for this reason recently Ayurveda medicine is fast moving. Since the
population of diabetes and cardiovascular affected patients are increasing rapidly, Ayurveda
treatments are used to reduce the risk of developing diabetes and cardiac issues further. In Sri
Lanka there are countless people who doesn’t have the knowledge on Ayurveda plants since for
many, these Ayurveda plants are hard to recognize correctly and requires an Ayurveda expert to
properly justify on these plants.
To overcome this issue, the research project proposes to build a mobile application containing an
image classification model to classify the Ayurveda plants using the leaves and predict the plants
which can be used in treatments for diabetes and cardiac issues. The proposed solution will
identify these plants and also give necessary details regarding the predicted plant on how these
plants are used, the methods of usage and for which health issue either diabetes or cardiac this
plant can be used for treatments. The application will contain a list of the Ayurveda plants and
details separately for the user to access as well. To achieve the prediction of the Ayurveda plants,
the author has used a CNN (Convolutional Neural Network) model for the classification part. For
this system author have gone with transfer learning choosing a MobileNet for classification. The
MobileNet have been altered by the author by adding additional layers such as convolution,
pooling and different other layers to achieve a higher accuracy. The model is trained with the
ImageNet weight to produce better performance.
Several models have been implemented to see which model outperforms the rest and finally
MobileNet have achieved the highest accuracy of 99%. All the models were trained from the
same dataset with the training, testing and validating data. Further these models can be more
improved to achieve high accuracies so these will be able to use for future studies."
