Abstract Medicinal Plant Identification using CNN
Classifying plant species has taken much attention in the research area to help people recognizing plants easily. In recent years, the convolutional neural networks (CNN) have achieved tremendous computer vision results, especially in image classification. Usually, humans find it difficult to recognize proper medicinal plants.
It requires the intuition of an expert botanist, which is a time-consuming manual task. In this research, we proposed an automated system for the medicinal plant classification, which will help people identify useful plant species quickly.
A new dataset of medicinal plants of India is introduced, collected from different regions across the country, and some state-of-the images collected from different sources. After that, a three-layer convolutional neural network is employed to extract the high-level features for the classification trained with the data augmentation technique.
Introduction to Medicinal plant identification using CNN
There are almost some plant species around the world. Among all the plants, some used in medicine, which provides many drugs from the ancient time to the present.
Within the context of India, there are about 449 enlisted medicinal plants. Among them, a lot of traditional and modern medicine exists which can be derived from these plants. Considering this huge number, the medicinal plant classification is a fairly difficult task and lengthy process even for experienced botanists.
Because it relies much on the inherited knowledge of an expert botanist. Also, the plants are hard to recognize because of their almost similar shape and color. The best of our Prediction, Remedies in this project. This paper, we introduce an image dataset of 6 classes of India medicinal plants which were taken in different conditions.
Also, a three-layer convolutional neural network is applied to classify the plants. In CNN, there are feature maps which capture the result of the filters to an input image.
H/w and S/W requirements
Computer : System.
Ram : 1GB
Rom : 32GB
Technology : Machine Learning.
Front End : GUI-tkinter.
IDLE : python 3.10.4
Virtual Envs : Anaconda