ArticleProceedings of the National Academy of Sciences of the United States of America2016
Computer vision cracks the leaf code.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed, 133 citations in OpenAlex.
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- What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods.Advances in neural information processing systems · 2022Article
- Geometric and topological approaches to shape variation inRoyal Society open science · 2021Article
- Review: Application of Artificial Intelligence in Phenomics.Sensors (Basel, Switzerland) · 2021Review
- An image dataset of cleared, x-rayed, and fossil leaves vetted to plant family for human and machine learning.PhytoKeys · 2021Article
- The Herbarium 2021 Half-Earth Challenge Dataset and Machine Learning Competition.Frontiers in plant science · 2021Article
- Composite modeling of leaf shape along shoots discriminatesApplications in plant sciences · 2020Article
- Machine-learning strategies for testing patterns of morphological variation in small samples: sexual dimorphism in gray wolf (Canis lupus) crania.BMC biology · 2020Article
- An algorithm competition for automatic species identification from herbarium specimens.Applications in plant sciences · 2020Article
- LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens.Applications in plant sciences · 2020Article
- High-ThroughputFrontiers in plant science · 2020Article
- Machine Learning Approaches to Improve Three Basic Plant Phenotyping Tasks Using Three-Dimensional Point Clouds.Plant physiology · 2019Article
- Automated Taxonomic Identification of Insects with Expert-Level Accuracy Using Effective Feature Transfer from Convolutional Networks.Systematic biology · 2019Article
- JPhyloIO: a Java library for event-based reading and writing of different phylogenetic file formats through a common interface.BMC bioinformatics · 2019Article
- Image-based classification of plant genus and family for trained and untrained plant species.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
Abstract
Understanding the extremely variable, complex shape and venation characters of angiosperm leaves is one of the most challenging problems in botany. Machine learning offers opportunities to analyze large numbers of specimens, to discover novel leaf features of angiosperm clades that may have phylogenetic significance, and to use those characters to classify unknowns. Previous computer vision approaches have primarily focused on leaf identification at the species level. It remains an open question whether learning and classification are possible among major evolutionary groups such as families and orders, which usually contain hundreds to thousands of species each and exhibit many times the foliar variation of individual species. Here, we tested whether a computer vision algorithm could use a database of 7,597 leaf images from 2,001 genera to learn features of botanical families and orders, then classify novel images. The images are of cleared leaves, specimens that are chemically bleached, then stained to reveal venation. Machine learning was used to learn a codebook of visual elements representing leaf shape and venation patterns. The resulting automated system learned to classify images into families and orders with a success rate many times greater than chance. Of direct botanical interest, the responses of diagnostic features can be visualized on leaf images as heat maps, which are likely to prompt recognition and evolutionary interpretation of a wealth of novel morphological characters. With assistance from computer vision, leaves are poised to make numerous new contributions to systematic and paleobotanical studies.
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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.