Calibrating UAV thermal sensors using machine learning methods for improved accuracy in agricultural applications

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dc.authoridKoksal, Eyup Selim/0000-0002-5103-9170en_US
dc.authoridTunca, Emre/0000-0001-6869-9602en_US
dc.authoridCetin Taner, Sakine/0000-0002-7333-4250en_US
dc.authorscopusid57204446671en_US
dc.authorscopusid24344113900en_US
dc.authorscopusid58341089000en_US
dc.authorwosidKoksal, Eyup Selim/IXD-8732-2023en_US
dc.authorwosidTunca, Emre/IQT-3077-2023en_US
dc.authorwosidCetin Taner, Sakine/JUV-5054-2023en_US
dc.contributor.authorTunca, Emre
dc.contributor.authorKoksal, Eyup Selim
dc.contributor.authorTaner, Sakine Cetin
dc.date.accessioned2024-08-23T16:04:43Z
dc.date.available2024-08-23T16:04:43Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractAccurate temperature measurements are essential for detecting crop stress, managing irrigation, and monitoring vegetation health. However, various factors can affect thermal sensors that can introduce measurement errors. To address this, machine learning (ML) algorithms were used to calibrate unmanned air vehicle (UAV) thermal sensor measurements. In this study, commercially available two different types of UAV thermal sensors, including Micasense Altum and Flir Duo Pro-R (FDP-R), have been tested and evaluated its performance by comparing the calibrated ground thermal measurements. For this purpose, five different ML algorithms, namely Random Forest, Support Vector Machine, K-NN and XGBoost, were used to calibrate UAV thermal sensors. Results showed that, after thermal calibration with XGBoost, the RMSE decreased by 2.84 degrees C (from 4.23 degrees C to 1.39 degrees C) for Micasense Altum and by 2.51 degrees C (from 3.84 degrees C to 1.33 degrees C) for FDP-R, while R2 increased from 0.89 to 0.96 for Micasense Altum and from 0.87 to 0.94 for FDP-R. In addition, we conducted correlation analyses between the calibrated temperature measurements and various sorghum phenotype parameters, such as leaf area index, crop height, and soil moisture. The results indicate that both sensors have performed well in terms of correlation coefficients. Micasense Altum has shown slightly better performance for crop height and soil moisture (r = -0.78 and r = -0.59, respectively), while FDP-R has performed better for leaf area index (r = -0.70). This study demonstrates the potential of using calibrated UAV thermal sensors for precision agriculture tasks and highlights the importance of validating the calibration with ground measurements.en_US
dc.description.sponsorshipScientific and Technical Research Council of Turkey (TUBITAK) [118O831]; COST (European Cooperation in Science and Technology)en_US
dc.description.sponsorshipThis study was supported by the Scientific and Technical Research Council of Turkey (TUBITAK, Project Number: 118O831) . The authors wish to thank the members of the Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu) . Their valuable feedback and insights greatly enriched our work and contributed to its overall quality.en_US
dc.identifier.doi10.1016/j.infrared.2023.104804
dc.identifier.issn1350-4495
dc.identifier.issn1879-0275
dc.identifier.scopus2-s2.0-85163945652en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.infrared.2023.104804
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14331
dc.identifier.volume133en_US
dc.identifier.wosWOS:001033876100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofInfrared Physics & Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectThermal camera calibrationen_US
dc.subjectMachine learningen_US
dc.subjectUAVen_US
dc.subjectAgricultural monitoringen_US
dc.subjectMicasense Altumen_US
dc.subjectFlir Duo Pro -Ren_US
dc.subjectWater-Stress Indexen_US
dc.subjectCropen_US
dc.subjectIrrigationen_US
dc.subjectTemperatureen_US
dc.subjectResolutionen_US
dc.subjectYielden_US
dc.subjectDripen_US
dc.titleCalibrating UAV thermal sensors using machine learning methods for improved accuracy in agricultural applicationsen_US
dc.typeArticleen_US

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