Dube, Timothy I.Sibanda, MbulisiMafuratidze, Pride2026-01-162026-01-162025Mafuratidze, 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.https://doi.org/10.1080/14498596.2025.2544143https://hdl.handle.net/10566/21732Shadows 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.enCast shadowCrop discriminationGlycine maxHue intensityRGB imageryArticle