Plant species identification from leaf patterns using histogram of oriented gradients feature space and convolution neural networks

Abstract

The determination of plant species from field observation requires substantial botanical expertise, which puts it beyond the reach of most nature enthusiasts. Traditional plant species identification is almost impossible for the general public and challenging even for professionals that deal with botanical problems daily, such as, conservationists, farmers, foresters, and landscape architects. Even for botanists themselves, species identification is often a difficult task. In this research, we proposed using two methods for the problem of plant species identification from leaf patterns. Firstly, we use a traditional recognition shallow architecture with extracted features histogram of oriented gradients (HOG) vector, then those features used to classifying by SVM algorithm. Secondly, we apply a deep convolutional neural network (CNN) for recognition purpose. We experimented on leaves data set in the Flavia leaf data set and the Swedish leaf data set. We want to compare a tradition method and a method consider as current state-of-the-art.

Keywords

• histogram of oriented gradients
• deep learning
• convolutional neural networks
• leaf classification