Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain

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

doi:10.1186/s13007-017-0245-8

Cited by 95 publications

(34 citation statements)

References 25 publications

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“…In fact, all top methods from the LifeCLEF 2017 contest, an event that aims to evaluate the performance of state-of-the-art identification tools for biological data, were based on deep learning 21 . CNNs have already successfully been used to identify plants from images of their leaves 22,23 and digitized images of herbaria 24 . CNNs could thus prove to be useful tools for taxonomists.…”

Section: Identification and Classificationmentioning

confidence: 99%

Applications for deep learning in ecology

Christin

Hervet

Lecomte

2018

Preprint

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A lot of hype has recently been generated around deep learning, a novel group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning has revolutionized several research fields such as bioinformatics and medicine with its flexibility and ability to process large and complex datasets. As ecological datasets are becoming larger and more complex, we believe these methods can be useful to ecologists as well. In this paper, we review existing implementations and show that deep learning has been used successfully to identify species, classify animal behaviour and estimate biodiversity in large datasets like camera‐trap images, audio recordings and videos. We demonstrate that deep learning can be beneficial to most ecological disciplines, including applied contexts, such as management and conservation. We also identify common questions about how and when to use deep learning, such as what are the steps required to create a deep learning network, which tools are available to help, and what are the requirements in terms of data and computer power. We provide guidelines, recommendations and useful resources, including a reference flowchart to help ecologists get started with deep learning. We argue that at a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be effectively processed by humans anymore, deep learning could become a powerful reference tool for ecologists.

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Section: Identification and Classificationmentioning

confidence: 99%

Applications for deep learning in ecology

Christin

Hervet

Lecomte

2018

Preprint

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“…While funding organizations are willing to support research into the direction of automated species identification and nature enthusiasts are helpful by contributing images, these resources are limited and should be efficiently utilised. Researchers as well as enthusiasts miss guidance on how to acquire suitable training images in an efficient way (Rzanny, Seeland, Wäldchen, & Mäder, ).…”

Section: Discussionmentioning

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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“…This, and other examples (e.g. Rzanny, Seeland, Wäldchen, & Mäder, 2017; Tabak et al., 2019), highlight the potential for deep learning to help to increase sample sizes, and therefore help resolve many limitations in power for biological studies (e.g. Wang et al., 2018).…”

Section: Introductionmentioning

confidence: 88%

Deep learning‐based methods for individual recognition in small birds

et al. 2020

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Individual identification is a crucial step to answer many questions in evolutionary biology and is mostly performed by marking animals with tags. Such methods are well‐established, but often make data collection and analyses time‐consuming, or limit the contexts in which data can be collected. Recent computational advances, specifically deep learning, can help overcome the limitations of collecting large‐scale data across contexts. However, one of the bottlenecks preventing the application of deep learning for individual identification is the need to collect and identify hundreds to thousands of individually labelled pictures to train convolutional neural networks (CNNs). Here we describe procedures for automating the collection of training data, generating training datasets, and training CNNs to allow identification of individual birds. We apply our procedures to three small bird species, the sociable weaver Philetairus socius, the great tit Parus major and the zebra finch Taeniopygia guttata, representing both wild and captive contexts. We first show how the collection of individually labelled images can be automated, allowing the construction of training datasets consisting of hundreds of images per individual. Second, we describe how to train a CNN to uniquely re‐identify each individual in new images. Third, we illustrate the general applicability of CNNs for studies in animal biology by showing that trained CNNs can re‐identify individual birds in images collected in contexts that differ from the ones originally used to train the CNNs. Finally, we present a potential solution to solve the issues of new incoming individuals. Overall, our work demonstrates the feasibility of applying state‐of‐the‐art deep learning tools for individual identification of birds, both in the laboratory and in the wild. These techniques are made possible by our approaches that allow efficient collection of training data. The ability to conduct individual recognition of birds without requiring external markers that can be visually identified by human observers represents a major advance over current methods.

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Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain

Cited by 95 publications

References 25 publications

Applications for deep learning in ecology

Applications for deep learning in ecology

Machine learning for image based species identification

Deep learning‐based methods for individual recognition in small birds

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