Enhancing target crop discrimination: a novel shadow detection technique for RGB datasets in mixed agricultural environments
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
Mapping Sciences Institute Australia
Abstract
Shadows 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.
Description
Keywords
Cast shadow, Crop discrimination, Glycine max, Hue intensity, RGB imagery
Citation
Mafuratidze, 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.