ArticleFrontiers in plant science2022
Using Deep Learning to Identify Costa Rican Native Tree Species From Wood Cut Images.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- DAM-Faster RCNN: few-shot defect detection method for wood based on dual attention mechanism.Scientific reports · 2025Article
- Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review.Biology · 2025Review
- Deep learning for Chilean native flora classification: a comparative analysis.Frontiers in plant science · 2023Article
- Article
- An Industrial-Grade Solution for Crop Disease Image Detection Tasks.Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tree species identification is critical to support their conservation, sustainable management and, particularly, the fight against illegal logging. Therefore, it is very important to develop fast and accurate identification systems even for non-experts. In this research we have achieved three main results. First, we developed-from scratch and using new sample collecting and processing protocols-an dataset called
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.