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      Automatic ladybird beetle detection using deep-learning models

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          Abstract

          Fast and accurate taxonomic identification of invasive trans-located ladybird beetle species is essential to prevent significant impacts on biological communities, ecosystem functions, and agricultural business economics. Therefore, in this work we propose a two-step automatic detector for ladybird beetles in random environment images as the first stage towards an automated classification system. First, an image processing module composed of a saliency map representation, simple linear iterative clustering superpixels segmentation, and active contour methods allowed us to generate bounding boxes with possible ladybird beetles locations within an image. Subsequently, a deep convolutional neural network-based classifier selects only the bounding boxes with ladybird beetles as the final output. This method was validated on a 2, 300 ladybird beetle image data set from Ecuador and Colombia obtained from the iNaturalist project. The proposed approach achieved an accuracy score of 92% and an area under the receiver operating characteristic curve of 0.977 for the bounding box generation and classification tasks. These successful results enable the proposed detector as a valuable tool for helping specialists in the ladybird beetle detection problem.

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Normalized cuts and image segmentation

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              Scikit-learn : machine learning in Python

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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: InvestigationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: Data curationRole: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2021
                10 June 2021
                : 16
                : 6
                : e0253027
                Affiliations
                [1 ] Colegio de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito USFQ, Quito, Ecuador
                [2 ] Museo de Zoología, Instituto iBIOTROP & Colegio de Ciencias Biológicas y Ambientales COCIBA, Universidad San Francisco de Quito USFQ, Quito, Ecuador
                [3 ] INESC TEC, Faculdade de Ciências da Universidade do Porto, Porto, Portugal
                [4 ] Department of Signal Theory and Communications and Telematic Systems and Computation, Rey Juan Carlos University, Fuenlabrada, Spain
                Vellore Institute of Technology: VIT University, INDIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                ‡ These authors also contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-9815-2659
                https://orcid.org/0000-0002-6132-2738
                https://orcid.org/0000-0003-3166-745X
                Article
                PONE-D-21-09868
                10.1371/journal.pone.0253027
                8191954
                34111201
                cb0ac00c-ae09-4cd4-9626-7aa9d9b70d34
                © 2021 Venegas et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 25 March 2021
                : 26 May 2021
                Page count
                Figures: 7, Tables: 2, Pages: 21
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100010654, Universidad San Francisco de Quito;
                Award ID: Grant no. 16870
                Award Recipient :
                NP. Collaboration Grants Program (Grant no. 16870), Universidad San Francisco de Quito (USFQ), https://www.usfq.edu.ec/ NP. Funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Zoology
                Entomology
                Insects
                Beetles
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Beetles
                Biology and Life Sciences
                Zoology
                Animals
                Invertebrates
                Arthropoda
                Insects
                Beetles
                Engineering and Technology
                Signal Processing
                Image Processing
                Research and Analysis Methods
                Imaging Techniques
                Biology and Life Sciences
                Zoology
                Entomology
                Insects
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Biology and Life Sciences
                Zoology
                Animals
                Invertebrates
                Arthropoda
                Insects
                Biology and Life Sciences
                Agriculture
                Pests
                Insect Pests
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Ecology and Environmental Sciences
                Species Colonization
                Invasive Species
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Custom metadata
                All data files available from https://osf.io/x6cv9/ Identifier: DOI 10.17605/OSF.IO/X6CV9.

                Uncategorized
                Uncategorized

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