Enhancing Seedling Detection in New Zealand Forestry: A Multi-Datastream Approach
Type of content
Conference Contributions - Other
UC permalink
Publisher's DOI/URI
Thesis discipline
Degree name
Publisher
Journal Title
Journal ISSN
Volume Title
Language
Date
2024
Authors
Singleton B
Xu C
Ye N
morgenroth, justin
Abstract
Description
Citation
Singleton B, Xu C, Ye N, Morgenroth J (2024). Enhancing Seedling Detection in New Zealand Forestry: A Multi-Datastream Approach. Rotorua, New Zealand: ForestSAT. 09/09/2024-13/09/2024.
Keywords
Ngā upoko tukutuku/Māori subject headings
ANZSRC fields of research
30 - Agricultural, veterinary and food sciences::3007 - Forestry sciences::300709 - Tree improvement (incl. selection and breeding)
30 - Agricultural, veterinary and food sciences::3007 - Forestry sciences::300707 - Forestry management and environment
30 - Agricultural, veterinary and food sciences::3007 - Forestry sciences::300704 - Forest health and pathology
46 - Information and computing sciences::4601 - Applied computing::460106 - Spatial data and applications
46 - Information and computing sciences::4603 - Computer vision and multimedia computation::460303 - Computational imaging
40 - Engineering::4007 - Control engineering, mechatronics and robotics::400703 - Autonomous vehicle systems
30 - Agricultural, veterinary and food sciences::3007 - Forestry sciences::300707 - Forestry management and environment
30 - Agricultural, veterinary and food sciences::3007 - Forestry sciences::300704 - Forest health and pathology
46 - Information and computing sciences::4601 - Applied computing::460106 - Spatial data and applications
46 - Information and computing sciences::4603 - Computer vision and multimedia computation::460303 - Computational imaging
40 - Engineering::4007 - Control engineering, mechatronics and robotics::400703 - Autonomous vehicle systems
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