Enhancing target crop discrimination: a novel shadow detection technique for RGB datasets in mixed agricultural environments

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dc.contributor.authorDube, Timothy I.
dc.contributor.authorSibanda, Mbulisi
dc.contributor.authorMafuratidze, Pride
dc.date.accessioned2026-01-16T07:05:03Z
dc.date.available2026-01-16T07:05:03Z
dc.date.issued2025
dc.description.abstractShadows pose significant challenges in smallholder farming systems, where mixed cropping is common. This study introduces two novel techniques: the Hue-Intensity-Green-Blue (HIGB) difference method for shadow detection and the Light Intensity Ratio-Based (LIRB) method for shadow compensation. Their performance was tested against the C3 and NSVDI models using five accuracy metrics on RGB imagery. HIGB consistently achieved superior accuracies (77–95%) compared to NSVDI (63–84%) and C3 (69–81%) in five different crop mixtures. Both the models, HIGB and LIRB, provide an integrated, robust solution for shadow detection and compensation in heterogeneous agricultural environments.
dc.identifier.citationMafuratidze, P., Mutanga, O., Masocha, M., Dube, T. and Sibanda, M., 2025. Enhancing target crop discrimination: a novel shadow detection technique for RGB datasets in mixed agricultural environments. Journal of Spatial Science, pp.1-16.
dc.identifier.urihttps://doi.org/10.1080/14498596.2025.2544143
dc.identifier.urihttps://hdl.handle.net/10566/21732
dc.language.isoen
dc.publisherMapping Sciences Institute Australia
dc.subjectCast shadow
dc.subjectCrop discrimination
dc.subjectGlycine max
dc.subjectHue intensity
dc.subjectRGB imagery
dc.titleEnhancing target crop discrimination: a novel shadow detection technique for RGB datasets in mixed agricultural environments
dc.typeArticle

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