EventsThe 2nd International Electronic Conference on Forests — Sustainable Forests: Ecology, Management, Products and Trade
Published
This submission belongs to the session S4. Forest Inventory, Modeling and Remote Sensing of the event The 2nd International Electronic Conference on Forests — Sustainable Forests: Ecology, Management, Products and Trade
Published date
17 Jun, 2022
Academic Editor
author-avatarLotus Guo
Citation
Shawn Carlisle Kefauver, Luisa Buchaillot, Joel Segarra, Jose Armando Fernandez Gallego, Mario Beltrán, Xavier Llosa, Míriam Piqué, José Luis Araus, Quantification of <em>Pinus pinea</em> pinecone productivity using machine learning of UAV and field images, in Proceedings of The 2nd International Electronic Conference on Forests — Sustainable Forests: Ecology, Management, Products and Trade, 1 September–15 September 2021, MDPI: Basel, Switzerland, doi: 10.3390/IECF2021-10789
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Quantification of Pinus pinea pinecone productivity using machine learning of UAV and field images

Joel Segarra 1,4
Mario Beltrán 6
Míriam Piqué 7
1. Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Avinguda Diagonal, 643, 08028 Barcelona Barcelona, Spain
2. AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain.
3. Programa de Ingenierıa Electronica, Facultad de Ingenierıa, Universidad de Ibague, Carrera 22 Calle 67, Ibague 730001, Colombia.
4. AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, Lleida 25198, Spain.
5. Consorci Forestal de Catalunya (CFC)
6. Centre de Ciència i Tecnologia Forestal de Catalunya (CTFC)
7. Joint Research Unit CTFC - Agrotecnio, Solsona, Spain
Abstract
Keywords
Pinus pinea
forest productivity
remote sensing
RGB
NIR
machine learning
Manuscript