A deep learning-based approach for detecting plant organs from digitized herbarium specimen images |
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| Institution: | 1. Faculty of Geosciences and Environment, University of Lausanne, CH-1015 Lausanne, Switzerland;2. Department of Geosciences and Geography, University of Helsinki, PO Box 64, 00014 Helsinki, Finland;1. Lushan Botanical Garden, Chinese Academy of Sciences, Jiangxi Province 332900, PR China;2. College of Forestry, Nanjing Forestry University, Nanjing 210037, PR China;3. Jiangsu Wiscom Technology Co. Ltd, Nanjing 211100, PR China;1. Research Scholar, Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore 560074, Karnataka, India;1. Informatics Department, Universitas Muhammadiyah Gresik, Gresik, Indonesia;2. Department of Computer Engineering, Faculty of Engineering, Gazi University, Ankara, Turkey;3. Computer Science, Informatics Institute, Gazi University, Ankara, Turkey;1. School of computer science, Yangtze University, Jingzhou 434023, China;2. Insect Ecology Laboratory, College of Agriculture, Yangtze University, Jingzhou 434025, China |
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| Abstract: | Herbarium specimens are excellent sources of botanical information to facilitate understanding and monitoring the evolution of plants and their effects on global climate change. Globally, many herbaria have undertaken digitization projects of herbarium specimens to preserve them and make them accessible in online repositories to botanists and ecologists. Automated detection of plant organs such as plant leaves, buds, flowers, and fruits on the digitized herbarium specimen images provides valuable information in various scientific contexts. We developed a deep learning approach based on the refined YOLO-V3 approach to detect plant organs within the digitized herbarium specimen images effectively. The proposed approach combines ResNet and DenseNet architectures to improve feature extraction capabilities. Also, a new scale of feature map is added to the existing scales to address the problem of YOLO-V3's low performance in detecting small plant organs. The experimental results demonstrate that our proposed approach can detect organs of different sizes within different specimens, where the precision and recall reached 94.2% and 95.5%, respectively. |
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