Parallel Algorithms for plant leaf recognition

Abstract

Plants play an important role in our life. Without plants, there will be no existence of the earth’s environment. Unfortunately in recent days, many types of plants are at risk of extinction. To protect plants a plant database is an important step towards the conservation of the earth’s biosphere. But the huge number of plant species worldwide with massive features needs fast computing solution. For handling such volumes of information, the development of a quick and efficient classification method has become an area of active research. Sparse representation for images is used for recognition in the last years in a wide range. On the other hand, applications of digital image processing for leaf recognition processing typically require massive computation power as the information required to be processed is vast. Thus, parallel algorithms are the solution that grants the capability to deal with huge datasets of plant leaves. Some motivations of this thesis are: • Providing a comparison between recent leaf recognition methods. • Designing and implementing a new parallel effective leaf recognition application. • Providing a comparison between the leaf recognition systems working sequentially against in parallel. In this thesis, we proposed parallel leaf recognition systems based on sparse representation and morphological features. It implicates new parallel algorithms for accomplishing different processes for leaf recognition. It utilizes the computation ability of GPUs to recognize leaves rapidly and accurately. This thesis consists of six chapters: Chapter one provides an introduction to the thesis. It contains definitions for plants, some known properties for leaf features. It also presents the concepts of parallel computing, the parallel models, GPUs, and Multi-cores.