Automatic flower detection and phenology monitoring using time‐lapse cameras and deep learning-Reference-Cited by-同舟云学术

Automatic flower detection and phenology monitoring using time‐lapse cameras and deep learning

Published:2022-06-02 Issue:6 Volume:8 Page:765-777
ISSN:2056-3485
Container-title:Remote Sensing in Ecology and Conservation
language:en
Short-container-title:Remote Sens Ecol Conserv

Author:

Mann Hjalte M. R.¹²^ORCID,Iosifidis Alexandros²^ORCID,Jepsen Jane U.³^ORCID,Welker Jeffrey M.⁴⁵⁶,Loonen Maarten J. J. E.⁷^ORCID,Høye Toke T.¹^ORCID

Affiliation:

1. Department of Ecoscience and Arctic Research Center Aarhus University C.F. Møllers Allé 8 8000 Aarhus C Denmark

2. Department of Electrical and Computer Engineering – Signal Processing and Machine Learning Aarhus University Finlandsgade 22 8200 Aarhus N Denmark

3. Department of Arctic Ecology, Fram Centre Norwegian Institute for Nature Research Hjalmar Johansens gt.14 9007 Tromsø Norway

4. Department of Ecology and Genetics University of Oulu Oulu Finland

5. University of the Arctic Rovaniemi Finland

6. Department of Biological Sciences University of Alaska Anchorage Alaska USA

7. Arctic Centre University of Groningen Groningen the Netherlands

Funder

Danmarks Frie Forskningsfond

National Science Foundation

Publisher

Wiley

Subject

Nature and Landscape Conservation,Computers in Earth Sciences,Ecology,Ecology, Evolution, Behavior and Systematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.275

Reference64 articles.

1. Abdulla W.(2017)Mask R‐CNN for object detection and instance segmentation on Keras and TensorFlow.GitHub Repository. Github. Available from:https://github.com/matterport/Mask_RCNN[Accessed 17th October 2019].

2. Automatic flower detection and classification system using a light‐weight convolutional neural network;Ärje J.;EUSIPCO Workshop on Signal Processing, Computer Vision and Deep Learning for Autonomous Systems,2019

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