Rosette Plant Centre Detection and Tracking Using YOLO: An Efficient Deep Learning Approach-Reference-Cited by-同舟云学术

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Rosette Plant Centre Detection and Tracking Using YOLO: An Efficient Deep Learning Approach

Published:2024-04-13 Issue: Volume: Page:1-6
ISSN:
Container-title:2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI)
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Author:

Akagić Amila¹,Sarić Rijad²,Buza Emir¹,Kecman Stefani¹,Lewsey Mathew G.²,Čustović Edhem³,Whelan James⁴

Affiliation:

1. University of Sarajevo (UNSA),Faculty of Electrical Engineering,Sarajevo,Bosnia and Herzegovina,71000

2. La Trobe Institute for Sustainable Agriculture & Food (LISAF),Dept. of Animal, Plant and Soil Sciences,Melbourne,VIC,Australia,3086

3. Scientific Instruments Australia (SIA),Melbourne,VIC,Australia,3086

4. College of Life Sciences, Zhejiang University,State Key Laboratory of Plant Environmental Resilience,Hangzhou,China,310058

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10585267/10585616/10586117.pdf?arnumber=10586117

Reference29 articles.

1. To meet grand challenges, agricultural scientists must engage in the politics of constructive collective action

2. Phenomics: the systematic study of phenotypes on a genome-wide scale

3. The Phenotyping Dilemma—The Challenges of a Diversified Phenotyping Community

4. Future Scenarios for Plant Phenotyping

5. Plant phenotyping: from bean weighing to image analysis

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