Automated plant species identification—Trends and future directions

Wäldchen, Jana; Rzanny, Michael; Seeland, Marco; Mäder, Patrick

doi:10.1371/journal.pcbi.1005993

Cited by 241 publications

(186 citation statements)

References 65 publications

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“…Automated approaches, such as computer vision and machine learning methods, can complement valuable citizen science data and may help bridge the “annotation gap” (Unger et al., ) between existing data and research‐ready data sets. Deep learning approaches, in particular, have been recently shown to achieve impressive performance on a variety of predictive tasks such as species identification (Joly et al., ; Wäldchen et al., ), plant trait recognition (Younis et al., ), plant species distribution modeling (Botella et al., ), and weed detection (Milioto et al., ). Carranza‐Rojas et al.…”

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confidence: 99%

Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras

et al. 2019

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Premise of the Study Phenological annotation models computed on large‐scale herbarium data sets were developed and tested in this study. Methods Herbarium specimens represent a significant resource with which to study plant phenology. Nevertheless, phenological annotation of herbarium specimens is time‐consuming, requires substantial human investment, and is difficult to mobilize at large taxonomic scales. We created and evaluated new methods based on deep learning techniques to automate annotation of phenological stages and tested these methods on four herbarium data sets representing temperate, tropical, and equatorial American floras. Results Deep learning allowed correct detection of fertile material with an accuracy of 96.3%. Accuracy was slightly decreased for finer‐scale information (84.3% for flower and 80.5% for fruit detection). Discussion The method described has the potential to allow fine‐grained phenological annotation of herbarium specimens at large ecological scales. Deeper investigation regarding the taxonomic scalability of this approach is needed.

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confidence: 99%

Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras

et al. 2019

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“…Research in this subject should be continued, and even though DL still has some obstacles to overcome (Marcus 2018), it has already advanced a lot (Guo et al 2016; Wäldchen et al 2018).…”

Section: Discussionmentioning

confidence: 99%

“…It is also common practice that deep neural networks outperform shallow neural networks (Chatfield et al 2014). In recent years, CNN technology has advanced greatly (LeCun et al 2015; Mishkin et al 2017; Wäldchen et al 2018), and many biological relevant studies have shown promising results (as can be read in the next Section: Related deep learning studies).…”

Section: Deep Learningmentioning

confidence: 99%

Taxonomic Classification of Ants (Formicidae) from Images using Deep Learning

Boer

Vos

2018

Preprint

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The well-documented, species-rich, and diverse group of ants (Formicidae) are important 1 ecological bioindicators for species richness, ecosystem health, and biodiversity, but ant 2 species identification is complex and requires specific knowledge. In the past few years, 3 insect identification from images has seen increasing interest and success, with processing 4 speed improving and costs lowering. Here we propose deep learning (in the form of a 5 convolutional neural network (CNN)) to classify ants at species level using AntWeb 6 images. We used an Inception-ResNet-V2-based CNN to classify ant images, and three 7 shot types with 10,204 images for 97 species, in addition to a multi-view approach, for 8 training and testing the CNN while also testing a worker-only set and an AntWeb 9 protocol-deviant test set. Top 1 accuracy reached 62% -81%, top 3 accuracy 80% -92%, 10 and genus accuracy 79% -95% on species classification for different shot type approaches. 11The head shot type outperformed other shot type approaches. Genus accuracy was broadly 12 similar to top 3 accuracy. Removing reproductives from the test data improved accuracy 13 only slightly. Accuracy on AntWeb protocol-deviant data was very low. In addition, we 14 make recommendations for future work concerning image threshold, distribution, and 15 quality, multi-view approaches, metadata, and on protocols; potentially leading to higher 16 accuracy with less computational effort. BOER & VOS

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“…In contrast, automated mammal and fish identification are done by taking images under field site condition (Weinstein, ). Automated plant identification is required under field as well as under lab conditions (Wäldchen, Rzanny, Seeland, & Mäder, ).…”

Section: Recent Research Studies Using Deep Learning For Species Idenmentioning

confidence: 99%

Machine learning for image based species identification

Wäldchen¹,

2018

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Accurate species identification is the basis for all aspects of taxonomic research and is an essential component of workflows in biological research. Biologists are asking for more efficient methods to meet the identification demand. Smart mobile devices, digital cameras as well as the mass digitisation of natural history collections led to an explosion of openly available image data depicting living organisms. This rapid increase in biological image data in combination with modern machine learning methods, such as deep learning, offers tremendous opportunities for automated species identification. In this paper, we focus on deep learning neural networks as a technology that enabled breakthroughs in automated species identification in the last 2 years. In order to stimulate more work in this direction, we provide a brief overview of machine learning frameworks applicable to the species identification problem. We review selected deep learning approaches for image based species identification and introduce publicly available applications. Eventually, this article aims to provide insights into the current state‐of‐the‐art in automated identification and to serve as a starting point for researchers willing to apply novel machine learning techniques in their biological studies. While modern machine learning approaches only slowly pave their way into the field of species identification, we argue that we are going to see a proliferation of these techniques being applied to the problem in the future. Artificial intelligence systems will provide alternative tools for taxonomic identification in the near future.

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Automated plant species identification—Trends and future directions

Cited by 241 publications

References 65 publications

Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras

Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras

Taxonomic Classification of Ants (Formicidae) from Images using Deep Learning

Machine learning for image based species identification

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