Convolutional Neural Network Approach for Different Leaf Classification

Abstract

There are millions of plant species with different shapes of a leaf. Those unfamiliar or outside the field may have difficulty recognizing the plant based on leaf appearances. A system that can provide an automatic response when a kind of leaf is exhibited may need to be developed. The system should provide the name of the leaf and other related information according to the input image. Therefore, in this paper, a research work on developing a system that can classify the leaf types is performed. The Convolutional Neural Network (CNN) architecture is applied with the help of TensorFlow for modeling the training data and testing. The classification accuracies are evaluated and tested on the leaf datasets where the unknown leaf image is used as input, and the name of the plant species belonging to the input image is classified as the system's output. The assessment showsthat the trained model can achieve a performance accuracy of more than 95%, which provides a promising system for the public to classify leaves and understand nature much more deeply.