ArticlePlant methods2019
Flowers, leaves or both? How to obtain suitable images for automated plant identification.
Article in Plant methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning in Plant Phenological Research: A Systematic Literature Review.Frontiers in plant science · 2022Pooled it
- Evaluating species identification apps as a tool for small plot-based surveys of vascular plants in Alberta, Canada.AoB PLANTS · 2026Article
- FloralArea: AI-powered algorithm for automated calculation of floral area from flower images to support plant and pollinator research.PloS one · 2025Article
- Geometric Morphometric Versus Genomic Patterns in a Large Polyploid Plant Species Complex.Biology · 2023Article
- Small Leaves, Big Diversity: Citizen Science and Taxonomic Revision Triples Species Number in the CarnivorousBiology · 2023Article
- Identifying and extracting bark key features of 42 tree species using convolutional neural networks and class activation mapping.Scientific reports · 2022Article
- Automatic detection of alien plant species in action camera images using the chopped picture method and the potential of citizen science.Breeding science · 2022Article
- A Performance Comparison of Classification Algorithms for Rose Plants.Computational intelligence and neuroscience · 2022Article
- Plant image identification application demonstrates high accuracy in Northern Europe.AoB PLANTS · 2021Article
- Citizen science and online data: Opportunities and challenges for snake ecology and action against snakebite.Toxicon: X · 2021Article
- Supervised Learning Computer Vision Benchmark for Snake Species Identification From Photographs: Implications for Herpetology and Global Health.Frontiers in artificial intelligence · 2021Article
- Image-Based Automated Recognition of 31 Poaceae Species: The Most Relevant Perspectives.Frontiers in plant science · 2021Article
- Crowdsourcing snake identification with online communities of professional herpetologists and avocational snake enthusiasts.Royal Society open science · 2021Article
- Flora Capture: a citizen science application for collecting structured plant observations.BMC bioinformatics · 2020Article
- What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora.AoB PLANTS · 2020Review
- Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDeep learning algorithms for automated plant identification need large quantities of precisely labelled images in order to produce reliable classification results. Here, we explore what kind of perspectives and their combinations contain more characteristic information and therefore allow for higher identification accuracy.
resultsWe developed an image-capturing scheme to create observations of flowering plants. Each observation comprises five in-situ images of the same individual from predefined perspectives (entire plant, flower frontal- and lateral view, leaf top- and back side view). We collected a completely balanced dataset comprising 100 observations for each of 101 species with an emphasis on groups of conspecific and visually similar species including twelve Poaceae species. We used this dataset to train convolutional neural networks and determine the prediction accuracy for each single perspective and their combinations via score level fusion. Top-1 accuracies ranged between 77% (entire plant) and 97% (fusion of all perspectives) when averaged across species. Flower frontal view achieved the highest accuracy (88%). Fusing flower frontal, flower lateral and leaf top views yields the most reasonable compromise with respect to acquisition effort and accuracy (96%). The perspective achieving the highest accuracy was species dependent.
conclusionsWe argue that image databases of herbaceous plants would benefit from multi organ observations, comprising at least the front and lateral perspective of flowers and the leaf top view.
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