To Örebro University

oru.seÖrebro University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Predicting Larch Casebearer damage with confidence using Yolo network models and conformal prediction
Örebro University, School of Science and Technology. Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.ORCID iD: 0000-0003-3107-331X
Örebro University, School of Science and Technology.ORCID iD: 0000-0003-3788-499X
2023 (English)In: Remote Sensing Letters, ISSN 2150-704X, E-ISSN 2150-7058, Vol. 14, no 10, p. 1023-1035Article in journal (Refereed) Published
Abstract [en]

This investigation shows that successful forecasting models for monitoring forest health status with respect to Larch Casebearer damages can be derived using a combination of a confidence predictor framework (Conformal Prediction) in combination with a deep learning architecture (Yolo v5). A confidence predictor framework can predict the current types of diseases used to develop the model and also provide indication of new, unseen, types or degrees of disease. The user of the models is also, at the same time, provided with reliable predictions and a well-established applicability domain for the model where such reliable predictions can and cannot be expected. Furthermore, the framework gracefully handles class imbalances without explicit over- or under-sampling or category weighting which may be of crucial importance in cases of highly imbalanced datasets. The present approach also provides indication of when insufficient information has been provided as input to the model at the level of accuracy (reliability) need by the user to make subsequent decisions based on the model predictions.

Place, publisher, year, edition, pages
Taylor & Francis, 2023. Vol. 14, no 10, p. 1023-1035
Keywords [en]
Yolo network, Larch Casebearer moth, conformal prediction, forest health, tree damage
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-108845DOI: 10.1080/2150704X.2023.2258460ISI: 001071044000001Scopus ID: 2-s2.0-85171885925OAI: oai:DiVA.org:oru-108845DiVA, id: diva2:1803845
Funder
Swedish Research Council, 2018-03807Available from: 2023-10-10 Created: 2023-10-10 Last updated: 2024-01-16Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Norinder, UlfLowry, Stephanie

Search in DiVA

By author/editor
Norinder, UlfLowry, Stephanie
By organisation
School of Science and Technology
In the same journal
Remote Sensing Letters
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 51 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf