Browsing by Author "Homan, Dewald"
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- ItemTree species identification and leaf segmentation from natural images using deep semi-supervised learning(Stellenbosch : Stellenbosch University, 2022-04) Homan, Dewald; Du Preez, Johan; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.ENGLISH ABSTRACT: Species identification is of significant importance to biodiversity conservation. However, there has been a sharp decline in expert species identification skills. This decline neces sitates automated tools for assisting accurate species identification. Earlier work on automated plant species classification focused on single plant at tributes with simple backgrounds. We advance automatic tree species identification by compiling a real-world natural image dataset for species identification. The multi-layered complexity of the dataset requires unconventional approaches for its utilisation. Deep semi-supervised learning (SSL) methods use labelled and additional unlabelled data for training a deep learning classifier. We present an SSL method for automated tree species identification from realistic, natural images. Our two-fold identification method exploits unlabelled images to perform tree feature recognition followed by species classi fication. The feature recognition step extracts bark and leaf images automatically from images with various tree features using minimal labelled data. We subsequently perform species classification of 50 chosen tree species and outperform traditional supervised learn ing (SL) approaches. Further, accurate image segmentation of leaves is critical for studying plant species characteristics. Current leaf segmentation algorithms are dependent on uniform leaf images or human interaction. Therefore, we propose an automated leaf segmentation method for extracting information from natural images. We employ our SSL feature recognition model for detection leaves and achieve state-of-the-art segmentation accuracy.