ArticleScientific reports2019
Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Analysis of Biological Images and Quantitative Monitoring Using Deep Learning and Computer Vision.Journal of imaging · 2026Review
- A Comprehensive Review of Remote Sensing and Artificial Intelligence Integration: Advances, Applications, and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Deep learning-based detection of indicator species for monitoring biodiversity in semi-natural grasslands.Environmental science and ecotechnology · 2024Article
- Verification study on how macrofungal fruitbody formation can be predicted by artificial neural network.Scientific reports · 2024Article
- Article
- Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance.Remote sensing · 2023Article
- The spatial distribution and expansion ofHeliyon · 2023Article
- Self-supervised learning for remote sensing scene classification under the few shot scenario.Scientific reports · 2023Article
- Image recognition of traditional Chinese medicine based on deep learning.Frontiers in bioengineering and biotechnology · 2023Article
- Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review.Frontiers in plant science · 2023Review
- The use of drones for mosquito surveillance and control.Parasites & vectors · 2022Review
- Artificial neural network-based model to predict the effect of γ-aminobutyric acid on salinity and drought responsive morphological traits in pomegranate.Scientific reports · 2022Article
- Automated mapping ofPeerJ · 2022Article
- Deep learning techniques to classify agricultural crops through UAV imagery: a review.Neural computing & applications · 2022Review
- Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework.Frontiers in plant science · 2022Article
- Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network.Scientific reports · 2021Article
- Deep learning and citizen science enable automated plant trait predictions from photographs.Scientific reports · 2021Article
- Method for accurate multi-growth-stage estimation of fractional vegetation cover using unmanned aerial vehicle remote sensing.Plant methods · 2021Article
- New tools for old problems - comparing drone- and field-based assessments of a problematic plant species.Environmental monitoring and assessment · 2021Article
- Flower Mapping in Grasslands With Drones and Deep Learning.Frontiers in plant science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies in high spatial detail. Efficient methods are needed to fully harness this unpreceded source of information for vegetation mapping. Deep learning algorithms such as Convolutional Neural Networks (CNN) are currently paving new avenues in the field of image analysis and computer vision. Using multiple datasets, we test a CNN-based segmentation approach (U-net) in combination with training data directly derived from visual interpretation of UAV-based high-resolution RGB imagery for fine-grained mapping of vegetation species and communities. We demonstrate that this approach indeed accurately segments and maps vegetation species and communities (at least 84% accuracy). The fact that we only used RGB imagery suggests that plant identification at very high spatial resolutions is facilitated through spatial patterns rather than spectral information. Accordingly, the presented approach is compatible with low-cost UAV systems that are easy to operate and thus applicable to a wide range of users.
Indexed as
Identifiers
What OpenQuestion holds
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.