Medicinal plant identification using convolutional neural network.

The world is filled with plants. They are a vital part of our ecosystem, providing food, shelter, and other services. Some objects have one use, while others have multiple uses. In this paper, we explore plants that have potential medical uses. Some of these plants have been used historically for medicinal purposes, while others may have new potential for treating health problems. Computer vision researchers have developed identification systems that help botanists pinpoint and unexplored plant genus more swiftly. Copious research has anchored on methods that magnify the utility of leaf databases for plant predictive modelling, but this can lead to leaf features that change depending on the data and extraction methodology used. We use Convolutional Neural Networks (CNNs) to unsheathe useful counsel from raw data representations. This allows us to understand the underlying structure of the data more easily. In this, we have used Medicinal leaf dataset from Mendeley Data which consist of 1500 data out of which 1471 data were used to train and validate the model. We were able to pull off an accuracy of 99.10 %. [ABSTRACT FROM AUTHOR]