Abstract
The identification of plant species by looking at their leaves, flowers, and seeds is a crucial component in the conservation of endangered plants. Traditional identification methods are manual and time consuming and require domain knowledge to operate. Owing to an increased interest in the automated plant identification system, we propose one that utilizes modern convolutional neural network architectures. This approach helps in the recognition of leaf images and can be integrated into mobile platforms like smartphones. Such a system can also be employed in aiding plant-related education, promoting ecotourism, and creating a digital heritage for plant species, among many others. Our proposed solution achieves a state-of-the-art performance for plant classification in the wild. An exhaustive set of experiments are performed to classify 112 species of plants from the challenging Indic-Leaf dataset. The best-performing model gives Top 1 precision of 90.08 and Top 5 precision of 96.90. We discuss and elaborate on our crowdsourcing web application that is used to collect and regulate data. We explain how the automated plant identification system can be integrated into a smartphone by detailing the flow of our mobile application.
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Muthireddy, V., Jawahar, C.V. (2021). Computer Vision for Capturing Flora. In: Mukhopadhyay, J., Sreedevi, I., Chanda, B., Chaudhury, S., Namboodiri, V.P. (eds) Digital Techniques for Heritage Presentation and Preservation. Springer, Cham. https://doi.org/10.1007/978-3-030-57907-4_12
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