Advanced leaf image retrieval via Multidimensional Embedding Sequence Similarity (MESS) method

Leaf identification using radial basis function neural networks and SSA based support vector machine

In this research, an efficient scheme to identify leaf types is proposed. In that scheme, the leaf boundary points are fitted in a continuous contour using Radial Basis Function Neural Networks (RBFNN) to calculate the centroid of the leaf shape. Afterwards, the distances between predetermined points and the centroid were computed and normalized. In addition, the time complexity of the features’ extraction algorithm was calculated. The merit of this scheme is objects’ independence to translation, rotation and scaling. Moreover, different classification techniques were evaluated against the leaf shape features. Those techniques included two of the most commonly used classification methods; RBFNN and SVM that were evaluated and compared with other researches that used complex features extraction algorithms with much higher dimensionality. Furthermore, a third classification method with an optimization technique for the SVM using Salp Swarm Algorithm (SSA) was utilized showing a significant improvement over RBFNN and SVM.

Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review

Species knowledge is essential for protecting biodiversity. The identification of plants by conventional keys is complex, time consuming, and due to the use of specific botanical terms frustrating for non-experts. This creates a hard to overcome hurdle for novices interested in acquiring species knowledge. Today, there is an increasing interest in automating the process of species identification. The availability and ubiquity of relevant technologies, such as, digital cameras and mobile devices, the remote access to databases, new techniques in image processing and pattern recognition let the idea of automated species identification become reality. This paper is the first systematic literature review with the aim of a thorough analysis and comparison of primary studies on computer vision approaches for plant species identification. We identified 120 peer-reviewed studies, selected through a multi-stage process, published in the last 10 years (2005-2015). After a careful analysis of these studies, we describe the applied methods categorized according to the studied plant organ, and the studied features, i.e., shape, texture, color, margin, and vein structure. Furthermore, we compare methods based on classification accuracy achieved on publicly available datasets. Our results are relevant to researches in ecology as well as computer vision for their ongoing research. The systematic and concise overview will also be helpful for beginners in those research fields, as they can use the comparable analyses of applied methods as a guide in this complex activity.