Multiclass insect counting through deep learning-based density maps estimation

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dc.contributor.authorBereciartua-Pérez, Arantza
dc.contributor.authorGómez, Laura
dc.contributor.authorPicón, Artzai
dc.contributor.authorNavarra-Mestre, Ramón
dc.contributor.authorKlukas, Christian
dc.contributor.authorEggers, Till
dc.contributor.institutionCOMPUTER_VISION
dc.date.issued2023-02
dc.descriptionPublisher Copyright: © 2022 The Authors
dc.description.abstractThe use of digital technologies and artificial intelligence techniques for the automation of some visual assessment processes in agriculture is currently a reality. Image-based, and recently deep learning-based systems are being used in several applications. Main challenge of these applications is to achieve a correct performance in real field conditions over images that are usually acquired with mobile devices and thus offer limited quality. Plagues control is a problem to be tackled in the field. Pest management strategies relies on the identification of the level of infestation. This degree of infestation is established through a counting task manually done by the field researcher so far. Current models were not able to appropriately count due to the small size of the insects and on the last year we presented a density map based algorithm that superseded state of the art methods for a single insect type. In this paper, we extend previous work into a multiclass and multi-stadia approach. Concretely, the proposed algorithm has been tested in two use cases: on the one hand, it counts five different types of adult individuals over multiple crop leaves; and on the other hand, it identifies four different stages for immatures over 2-cm leaf disks. In these leaf disks, some of the species are in different stadia being some of them micron size and difficult to be identified even for the non-expert user. The proposed method achieves good results in both cases. The model for counting adult insects in a leaf achieves a RMSE ranging from 0.89 to 4.47, MAE ranging from 0.40 to 2.15, and R2 ranging from 0.86 to 0.91 for 4 different species in its adult phase (BEMITA, FRANOC, MYZUPE and APHIGO) that may appear together in the same leaf. Besides, for FRANOC, two stadia nymphs and adults are considered. The model developed for counting BEMITA immatures in 2-cm disks obtains R2 values up to 0.98 for big nymphs. This solution was embedded in a docker and can be accessed through an app via REST service in mobile devices. It has been tested in the wild under real conditions in different locations worldwide and over 14 different crops.en
dc.description.statusPeer reviewed
dc.format.extent1
dc.format.extent19738687
dc.identifier.citationBereciartua-Pérez , A , Gómez , L , Picón , A , Navarra-Mestre , R , Klukas , C & Eggers , T 2023 , ' Multiclass insect counting through deep learning-based density maps estimation ' , Smart Agricultural Technology , vol. 3 , 100125 , pp. 100125 . https://doi.org/10.1016/j.atech.2022.100125
dc.identifier.doi10.1016/j.atech.2022.100125
dc.identifier.issn2772-3755
dc.identifier.otherresearchoutputwizard: 11556/1429
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85139334597&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofSmart Agricultural Technology
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsObject Detection
dc.subject.keywordsDeep Learning
dc.subject.keywordsIOU
dc.subject.keywordsInsect counting
dc.subject.keywordsObject Detection
dc.subject.keywordsDeep Learning
dc.subject.keywordsIOU
dc.subject.keywordsInsect counting
dc.subject.keywordsComputer Science (miscellaneous)
dc.subject.keywordsGeneral Agricultural and Biological Sciences
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsSDG 3 - Good Health and Well-being
dc.subject.keywordsFunding Info
dc.subject.keywordsThe authors would like to thank all field researchers that generated the dataset, carried out the annotation process, performed the validation in the wild, and in general, supported the work in Tecnalia and BASF specially to Javier Romero, Carlos Javier Jim ́enez, Amaia Ortiz, Aitor_x000D_ Alvarez and Jone Echazarra.
dc.subject.keywordsThe authors would like to thank all field researchers that generated the dataset, carried out the annotation process, performed the validation in the wild, and in general, supported the work in Tecnalia and BASF specially to Javier Romero, Carlos Javier Jim ́enez, Amaia Ortiz, Aitor_x000D_ Alvarez and Jone Echazarra.
dc.titleMulticlass insect counting through deep learning-based density maps estimationen
dc.typejournal article
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