LifeCLEF 2017 Lab Overview: Multimedia Species Identification Challenges

Joly, Alexis; Goëau, Hervé; Glotin, Hervé; Spampinato, Concetto; Bonnet, Pierre; Vellinga, Willem-Pier; Lombardo, Jean-Christophe; Planquè, Robert; Palazzo, Simone; Müller, Henning

doi:10.1007/978-3-319-65813-1_24

Cited by 51 publications

(32 citation statements)

References 20 publications

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“…Automating the analysis process to identify and classify the data has therefore become necessary and deep learning methods have proven to be an effective solution. 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 .…”

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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“…Due to an increasing interest in applying computer vision to agriculture, a number of data sets dedicated to plants have been released (Giselsson, Dyrmann, Jørgensen, Jensen, & Midtiby, 2017;Haug & Ostermann, 2015;Minervini, Fischbach, Scharr, & Tsaftaris, 2016;Mureşan & Oltean, 2018;Sa et al, 2016). Notably, the PlantCLEF (Joly et al, 2017) data set spans many species of plant. While robotics benefits from developments in computer vision and machine learning, the domain of data used in these communities is unlikely to properly represent the specific nature of robotics applications.…”

Section: Introductionmentioning

confidence: 99%

A high‐resolution, multimodal data set for agricultural robotics: A Ladybird's‐eye view of Brassica

Bender

Whelan

Sukkarieh

2019

Journal of Field Robotics

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This article presents an agricultural data set collected by Ladybird, an autonomous field robot designed at the Australian Centre for Field Robotics. The data set contains weekly scans of cauliflower and broccoli (Brassica oleracea) covering a 10 week growth cycle from transplant to harvest. The data set includes ground truth; physical characteristics of the crop; environmental data collected by a weather station and a soil sensor network; and scans of the crop performed by Ladybird, which include stereo color, thermal and hyperspectral imagery. The layout of the farm and data collection methodology are described. A description of Ladybird's capabilities and sensors are provided. Benchmark results are provided to illustrate the contents of the data set and how it can be processed. The hyperspectral data are compiled into hypercubes and the pixels are classified into crop, weed, or soil. An object detector is applied to the color imagery to locate the crop. Our intention in releasing the data set is to facilitate robotics and machine learning research activity in agriculture. The data set can be downloaded from https://doi.org/10.25910/5c941d0c8bccb.

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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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LifeCLEF 2017 Lab Overview: Multimedia Species Identification Challenges

Cited by 51 publications

References 20 publications

Applications for deep learning in ecology

Applications for deep learning in ecology

A high‐resolution, multimodal data set for agricultural robotics: A Ladybird's‐eye view of Brassica

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

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