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Sensor-Based Yield Prediction in Durum Wheat Under Semi-Arid Conditions Using Machine Learning Across Zadoks Growth Stages

2025-07-12Artigo científico Acesso aberto
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dc.contributor.authorRufaioğlu, Süreyya Betül
dc.contributor.authorBilgili, Ali Volkan
dc.contributor.authorSavaşlı, Erdinç
dc.contributor.authorÖzberk, İrfan
dc.contributor.authorAydemir, Salih
dc.contributor.authorIsmael, Amjad Mohamed
dc.contributor.authorKaya, Yunus
dc.contributor.authorMatos-Carvalho, João P.
dc.contributor.institutionCTS - Centro de Tecnologia e Sistemas
dc.contributor.institutionUNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
dc.contributor.pblMolecular Diversity Preservation International (MDPI)
dc.date.accessioned2026-01-14T15:52:58Z
dc.date.available2026-01-14T15:52:58Z
dc.date.issued2025-07-12
dc.descriptionPublisher Copyright: © 2025 by the authors.
dc.description.abstractYield prediction in wheat cultivated under semi-arid climatic conditions is gaining increasing importance for sustainable production strategies and decision support systems. In this study, a time-series-based modeling approach was implemented using sensor-based data (SPAD, NSPAD, NDVI, INSEY, and plant height measurements collected at four different Zadoks growth stages (ZD24, ZD30, ZD31, and ZD32). Five different machine learning algorithms (Random Forest, Gradient Boosting, AdaBoost, LightGBM, and XGBoost) were tested individually for each stage, and the model performances were evaluated using statistical metrics such as R2%, RMSE t/ha, and MAE t/ha. Modeling results revealed that the ZD31 stage (first node detectable) was identified as the most successful phase for prediction accuracy, with the XGBoost model achieving the highest R2% score (81.0). In the same model, RMSE and MAE values were calculated as 0.49 and 0.37, respectively. The LightGBM model also showed remarkable performance during the ZD30 stage, achieving an R2% of 78.0, an RMSE of 0.52, and an MAE of 0.40. The SHAP (SHapley Additive exPlanations) method used to interpret feature importance revealed that the NDVI and INSEY indices contributed the most significant values to prediction accuracy for yield. This study demonstrates that phenology-sensitive yield prediction approaches offer high potential for sensor-based digital applications. Furthermore, the integration of timing, model selection, and explainability provided valuable insights for the development of advanced decision support systems.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent27
dc.format.extent10886598
dc.identifier.doi10.3390/rs17142416
dc.identifier.issn2072-4292
dc.identifier.otherPURE: 147533637
dc.identifier.otherPURE UUID: 3fa158ea-cf2e-4bb8-84c1-1931b7b6b4b4
dc.identifier.otherScopus: 105011683070
dc.identifier.otherWOS: 001536020000001
dc.identifier.urihttp://hdl.handle.net/10362/199017
dc.identifier.urlhttps://www.scopus.com/pages/publications/105011683070
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001536020000001
dc.language.isoeng
dc.peerreviewedyes
dc.subjectMachine learning
dc.subjectSensor-based data
dc.subjectSHAP analysis
dc.subjectWheat
dc.subjectYield prediction
dc.subjectZadoks stages
dc.subjectGeneral Earth and Planetary Sciences
dc.titleSensor-Based Yield Prediction in Durum Wheat Under Semi-Arid Conditions Using Machine Learning Across Zadoks Growth Stagesen
dc.typejournal article
degois.publication.firstPage1
degois.publication.issue14
degois.publication.lastPage27
degois.publication.titleRemote Sensing
degois.publication.volume17
dspace.entity.typePublication
rcaap.rightsopenAccess

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