Exploring multiple algorithms for leaf classification: A comparative study

Abstract

In the world there are an estimated nearly half a million plant species, and the leaves of each plant have their own unique characteristics. However, leaf classification has historically been problematic, with some similar-looking leaves being repeatedly identified and errors may even occur. In the past, people used precision instruments, chemical analysis, and other methods to improve the success rate of resolution, but this often required a certain amount of professional knowledge, sometimes it was trouble to operate. And the development of computer in recent years makes the leaf classification problem can be solved better through the image processing and machine learning technology. Therefore, the topic of this paper is to classify leaves by different algorithms and compare the classification results, which include KNN, Random Forest, the neural network algorithm ANN, and CNN. It can be seen through experiments that the classification results obtained by ANN have a higher accuracy rate among these algorithms.