Applying machine learning to investigate long‐term insect–plant interactions preserved on digitized herbarium specimens

Meineke, Emily K.; Tomasi, Carlo; Yuan, Shuai; Pryer, Kathleen M.

doi:10.1002/aps3.11369

Cited by 26 publications

(18 citation statements)

References 61 publications

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“…All of these approaches could greatly enrich our capacity to report the presence and abundance of reproductive structures in herbarium collections, and without doubt could contribute to enlarging the huge research potential of these invaluable collections. Combined with other machine learning techniques (such as in the studies of Meineke et al, 2020;Ott et al, 2020;Pryer et al, 2020;Weaver et al, 2020;White et al, 2020), they could largely increase the benefits of using the newly digitized herbarium data sets.…”

Section: Discussionmentioning

confidence: 99%

A new fine‐grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction

et al. 2020

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Herbarium specimens represent an outstanding source of material with which to study plant phenological changes in response to climate change. The fine-scale phenological annotation of such specimens is nevertheless highly time consuming and requires substantial human investment and expertise, which are difficult to rapidly mobilize. METHODS:We trained and evaluated new deep learning models to automate the detection, segmentation, and classification of four reproductive structures of Streptanthus tortuosus (flower buds, flowers, immature fruits, and mature fruits). We used a training data set of 21 digitized herbarium sheets for which the position and outlines of 1036 reproductive structures were annotated manually. We adjusted the hyperparameters of a mask R-CNN (regional convolutional neural network) to this specific task and evaluated the resulting trained models for their ability to count reproductive structures and estimate their size. RESULTS:The main outcome of our study is that the performance of detection and segmentation can vary significantly with: (i) the type of annotations used for training, (ii) the type of reproductive structures, and (iii) the size of the reproductive structures. In the case of Streptanthus tortuosus, the method can provide quite accurate estimates (77.9% of cases) of the number of reproductive structures, which is better estimated for flowers than for immature fruits and buds. The size estimation results are also encouraging, showing a difference of only a few millimeters between the predicted and actual sizes of buds and flowers.DISCUSSION: This method has great potential for automating the analysis of reproductive structures in high-resolution images of herbarium sheets. Deeper investigations regarding the taxonomic scalability of this approach and its potential improvement will be conducted in future work.

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Section: Discussionmentioning

confidence: 99%

A new fine‐grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction

et al. 2020

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“…There is also a valuable archive of entomological data in herbarium specimens in the form of signs of herbivory (68). The standard digitization of herbarium collections has proven suitable for extracting herbivory data using machine learning techniques (69). Techniques to automate digitization will accelerate the development of such valuable databases (64).…”

Section: Sensor-based Insect Monitoringmentioning

confidence: 99%

Deep learning and computer vision will transform entomology

Høye

Ärje

Bjerge

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

305

206

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Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.

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“…Coastal tourism monitoring (Kubo et al, 2020) can be integrated with traffic data (Hu et al, 2020) to optimize traffic management and thereby reduce pollutant emissions. The effect of transport on plant damage can be included (Meineke et al, 2020) as a factor to be analyzed, or we can use it (Gurram et al, 2019) to identify patterns in population movement.…”

Section: The Importance Of the System Of Systems Approachmentioning

confidence: 99%

“…Population movements (Gurram et al, 2019) affect water consumption (Mourtzios et al, 2021), can damage plants (Meineke et al, 2020), show the popularity of coastal areas (Kubo et al, 2020), but are also suitable for improving transport planning (Hu et al, 2020). Because the movement of residents is closely related to the infrastructure (Milojevic-Dupont et al, 2020), it is a very valuable input in urban planning.…”

Section: The Importance Of the System Of Systems Approachmentioning

confidence: 99%

“…For the sustainability of life below water (SDG14), marine life prediction models (Coro et al, 2020) and human coastal activity (Kubo et al, 2020) can be integrated. Of course, the goal of life on land (SDG15) also requires new research, where a satellite-based study of agriculture and forestry (Majidi et al, 2021), deployment of IoT sensors (Lambrinos, 2019), analysis of climatic factors of potato damage (Fenu and Malloci, 2019), studying the morphology of plants (Meineke et al, 2020), or social media based illustration of palm oil consumption (Teng et al, 2020) are promising. Partnerships for the goals (SDG17) is critical in several ways, on the one hand we recommend the grouping of climate services (Howard et al, 2020), which fits the SoS concept we propose, and on the other hand we need to integrate the knowledge and give feedback to society.…”

Section: The Importance Of the System Of Systems Approachmentioning

confidence: 99%

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The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

Sebestyén

Czvetkó

Abonyi

2021

Front. Environ. Sci.

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The aim of this paper is to provide an overview of the interrelationship between data science and climate studies, as well as describes how sustainability climate issues can be managed using the Big Data tools. Climate-related Big Data articles are analyzed and categorized, which revealed the increasing number of applications of data-driven solutions in specific areas, however, broad integrative analyses are gaining less of a focus. Our major objective is to highlight the potential in the System of Systems (SoS) theorem, as the synergies between diverse disciplines and research ideas must be explored to gain a comprehensive overview of the issue. Data and systems science enables a large amount of heterogeneous data to be integrated and simulation models developed, while considering socio-environmental interrelations in parallel. The improved knowledge integration offered by the System of Systems thinking or climate computing has been demonstrated by analysing the possible inter-linkages of the latest Big Data application papers. The analysis highlights how data and models focusing on the specific areas of sustainability can be bridged to study the complex problems of climate change.

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Applying machine learning to investigate long‐term insect–plant interactions preserved on digitized herbarium specimens

Cited by 26 publications

References 61 publications

A new fine‐grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction

A new fine‐grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction

Deep learning and computer vision will transform entomology

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

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