Accurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral data

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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.authoridAKAY, HASAN/0000-0003-1198-8686en_US
dc.authorscopusid57204446671en_US
dc.authorscopusid24344113900en_US
dc.authorscopusid57214484479en_US
dc.authorscopusid56868366700en_US
dc.authorscopusid58341089000en_US
dc.authorwosidKoksal, Eyup Selim/IXD-8732-2023en_US
dc.authorwosidTunca, Emre/IQT-3077-2023en_US
dc.authorwosidAKAY, HASAN/T-9305-2018en_US
dc.authorwosidCetin Taner, Sakine/JUV-5054-2023en_US
dc.contributor.authorTunca, Emre
dc.contributor.authorKoksal, Eyup Selim
dc.contributor.authorOzturk, Elif
dc.contributor.authorAkay, Hasan
dc.contributor.authorTaner, Sakine Cetin
dc.date.accessioned2024-08-23T16:07:09Z
dc.date.available2024-08-23T16:07:09Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThis study investigates the effects of different water stress levels on spectral information, leaf area index (LAI), and the performance of three machine learning (ML) algorithms in estimating crop water content (CWC) of sorghum. The results show that the spectral reflectance of sorghum varies with growth stage and irrigation treatment, but consistent patterns are observed for each treatment. The LAI of sorghum gradually increased throughout the growth stages, with the most significant variation observed during the flowering stage. In this study, three machine learning-based regression models, namely, extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM), were utilized to estimate sorghum CWC using hyperspectral measurements. Recursive feature elimination (RFE) method was used to select the optimal spectral reflectance wavelengths for the ML models, and principal component analysis (PCA) was used to reduce the dimensionality of the hyperspectral data. The results indicated that the RF model achieved the highest R-2 (0.90) and lowest of RMSE (56.05) value using selected wavelengths, while the XGBoost model demonstrated superior accuracy and reliability in estimating CWC using dimensionality-reduced hyperspectral data (r = 0.96, RMSE = 45.77). Also, the study highlights the importance of vegetation index (VI) in CWC estimate. Some VIs, such as NDVI and MSAVI, performed poorly, while others, such as CL_Rededge and EVI, performed better. The study provides valuable insights into the effects of water stress levels on spectral information, LAI, and the performance of ML algorithms in estimating the CWC of sorghum. The findings have significant implications for precision agriculture, as accurate and reliable estimates of CWC can help farmers optimize irrigation and fertilizer applications, leading to improved crop yields and resource efficiency.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey [118O831]en_US
dc.description.sponsorshipThis study was supported by The Scientific and Technological Research Council of Turkey (118O831).en_US
dc.identifier.doi10.1007/s10661-023-11536-8
dc.identifier.issn0167-6369
dc.identifier.issn1573-2959
dc.identifier.issue7en_US
dc.identifier.pmid37353582en_US
dc.identifier.scopus2-s2.0-85162745100en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s10661-023-11536-8
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14524
dc.identifier.volume195en_US
dc.identifier.wosWOS:001018570500007en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofEnvironmental Monitoring and Assessmenten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCrop water contenten_US
dc.subjectHyperspectralen_US
dc.subjectMLen_US
dc.subjectLAIen_US
dc.subjectVegetation indicesen_US
dc.subjectRecursive Feature Eliminationen_US
dc.subjectLeaf-Area Indexen_US
dc.subjectSpectral Reflectanceen_US
dc.subjectDimension Reductionen_US
dc.subjectChlorophyll Contenten_US
dc.subjectAlgorithmsen_US
dc.subjectYielden_US
dc.subjectSpectroscopyen_US
dc.subjectCanopiesen_US
dc.subjectSystemsen_US
dc.titleAccurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral dataen_US
dc.typeArticleen_US

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