ArticleFrontiers in plant science2021
Flower Mapping in Grasslands With Drones and Deep Learning.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- Remote Sensing of Grassland Plant Biodiversity and Functional Traits.Ecology and evolution · 2025Review
- Drones in ecology: ten years back and forth.Bioscience · 2025Article
- Ground-truthed and high-resolution drone images of the leafy spurge weed plant (Euphorbia esula).Scientific data · 2025Article
- High resolution descriptors for UAV mapping in biodiversity conservation - A case study of sandy steppe habitat renewal.PloS one · 2025Article
- Bridging technology and ecology: enhancing applicability of deep learning and UAV-based flower recognition.Frontiers in plant science · 2025Article
- Phenological responses of alpine snowbed communities to advancing snowmelt.Ecology and evolution · 2024Article
- Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach.Scientific reports · 2024Article
- Article
- Animal board invited review: Grassland-based livestock farming and biodiversity.Animal : an international journal of animal bioscience · 2022Review
- Bee Tracker-an open-source machine learning-based video analysis software for the assessment of nesting and foraging performance of cavity-nesting solitary bees.Ecology and evolution · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Manual assessment of flower abundance of different flowering plant species in grasslands is a time-consuming process. We present an automated approach to determine the flower abundance in grasslands from drone-based aerial images by using deep learning (Faster R-CNN) object detection approach, which was trained and evaluated on data from five flights at two sites. Our deep learning network was able to identify and classify individual flowers. The novel method allowed generating spatially explicit maps of flower abundance that met or exceeded the accuracy of the manual-count-data extrapolation method while being less labor intensive. The results were very good for some types of flowers, with precision and recall being close to or higher than 90%. Other flowers were detected poorly due to reasons such as lack of enough training data, appearance changes due to phenology, or flowers being too small to be reliably distinguishable on the aerial images. The method was able to give precise estimates of the abundance of many flowering plant species. In the future, the collection of more training data will allow better predictions for the flowers that are not well predicted yet. The developed pipeline can be applied to any sort of aerial object detection problem.
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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.