2017/01/28 by İlke Çuğu, Eren Şener, Çuğu, İlke +11 · 1 citation
Agricultural and Biological Sciences · Computer Science · #68-06 #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Neural Networks and Applications #Smart Agriculture and AI #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.1701.08291
openalex publication_date 2017/01/28 · openalex created_date 2017/02/10 · openalex updated_date 2026/07/28
We propose a novel tree classification system called Treelogy, that fuses deep representations with hand-crafted features obtained from leaf images to perform leaf-based plant classification. Key to this system are segmentation of the leaf from an untextured background, using convolutional neural networks (CNNs) for learning deep representations, extracting hand-crafted features with a number of image processing techniques, training a linear SVM with feature vectors, merging SVM and CNN results, and identifying the species from a dataset of 57 trees. Our classification results show that fusion of deep representations with hand-crafted features leads to the highest accuracy. The proposed algorithm is embedded in a smart-phone application, which is publicly available. Furthermore, our novel dataset comprised of 5408 leaf images is also made public for use of other researchers.