J
Jana Wäldchen
Researcher at Max Planck Society
Publications - 23
Citations - 1487
Jana Wäldchen is an academic researcher from Max Planck Society. The author has contributed to research in topics: Automated species identification & Plant identification. The author has an hindex of 13, co-authored 23 publications receiving 927 citations.
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Journal ArticleDOI
Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review
Jana Wäldchen,Patrick Mäder +1 more
TL;DR: 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, identifying 120 peer-reviewed studies published in the last 10 years.
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Recommending plant taxa for supporting on-site species identification
TL;DR: It is found that occurrence records are complementary to presence-absence data and using both in combination yields considerably higher recall of 96% along with improved ranking metrics, and a spatio-temporal prior can substantially expedite the overall identification problem.
Journal ArticleDOI
Machine learning for image based species identification
Jana Wäldchen,Patrick Mäder +1 more
TL;DR: This paper focuses on deep learning neural networks as a technology that enabled breakthroughs in automated species identification in the last 2 years and argues that the authors are going to see a proliferation of these techniques being applied to the problem in the future.
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Automated plant species identification-Trends and future directions.
TL;DR: The technical status quo on computer vision approaches for plant species identification is reviewed, the main research challenges to overcome in providing applicable tools are highlighted, and a discussion of open and future research thrusts is discussed.
Journal ArticleDOI
Plant species classification using flower images-A comparative study of local feature representations.
TL;DR: This study provides a comprehensive overview of competing techniques and the implications of their main parameters for flower-based plant species classification, finding that true local feature detectors in combination with advanced encoding methods yield higher classification results at lower computational costs compared to commonly used dense sampling and spatial pooling methods.