Plant Leaf Image Recognition using Multiple-grid Based Local Descriptor and Dimensionality Reduction Approach

What is it about?

The identification process of plant species is one of the significant and challenging problems. In this research area, many researchers have focused on identifying the plant leaf images because the leaves of a plant are found almost all year round. The achieve method of the plant leaf image recognition is based on unique extraction features from the plant leaf and using the well-known machine learnings as a classification method. As a result, recognition accuracy was often not very high. In order to improve recognition accuracy, we proposed a multiple grids technique based on the local descriptors and dimensionality reduction. Firstly, we divided the plant leaf image according to grid size and calculated the local descriptors from each grid. Secondly, the dimensionality reduction is proposed to transform and decrease the correlated variables of the feature vector. Finally, the feature vector with a relatively low-dimensional is transferred to the machine learning techniques, which are the support vector machine and multi-layer perceptron algorithms. We have evaluated and compared the proposed algorithm with the bag of visual words method and the deep convolutional neural network (including AlexNet and GoogLeNet architectures) on the Folio leaf image dataset. The experiments show that the proposed algorithm has improved and obtained very high accuracy on plant leaf image recognition.

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Photo by Jaimie Phillips on Unsplash

Photo by Jaimie Phillips on Unsplash

Why is it important?

The research focuses on the importance of plant leaf recognition by experiment with (Folio dataset) which collects 32 different species of plants. This research presents multiple grids and dimensionality reduction based descriptors approach, which is simple but effective. The multiple grids divide plant leaves into sub-regions, then it brings the sub-region to calculate the special features using various feature extraction techniques that pull out the distinctive characteristics of the plant leaves. The methods are a histogram of oriented gradients (HOG), local binary pattern (LBP), and color histogram. Finally, the feature will be fed to the dimensionality reduction method by using principal component analysis (PCA) in order to reduce the feature vector size of each method. The size reductions have direct effect on training time and increase the recognition efficiency as well. In this paper, the feature vector was used in training and recognition by a support vector machine (SVM) and Multi-layer perceptron (MLP). This method obtained a very high recognition rate when compared to the deep learning method.