Leaf recognition using convolutional neural networks based features

Abstract

There is a warning light for the loss of plant habitats worldwide that entails concerted efforts to conserve plant biodiversity. Thus, plant species classification is crucial to address this environmental challenge. In recent years, there has been a considerable increase in studies related to plant taxonomy. While some researchers try to improve their recognition performance using novel approaches, others concentrate on computational optimization for their framework. In addition, a few studies are diving into feature extraction to gain significantly in terms of accuracy. This paper proposes an effective method for the leaf recognition problem. In our proposed approach, a leaf goes through some pre-processing to extract its refined color image, vein image, xy-projection histogram, handcrafted shape, texture features, and Fourier descriptors. These attributes are then transformed into a better representation by neural network-based encoders before a support vector machine (SVM) model is utilized to classify different leaves. Overall, our approach performs a state-of-the-art result on the Flavia leaf dataset, achieving the accuracy of 99.69% on test sets under random 10-fold cross-validation and bypassing the previous methods. Another important contribution is the trade-offs in classification performance while minimizing the feature categories used. In order to tackle this challenge, we designed several empirical experiments to analyze the performance of different combinations of feature sources and choose the best combination for features for the main problem. We also release our codes (Scripts are available at https://github.com/Tayerquach/flavia_recognition) for contributing to the research community in the leaf classification problem.