Enhancing target crop discrimination: a novel shadow detection technique for RGB datasets in mixed agricultural environments
Year: 2025
Type: article
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.
Source: Figshare
Authors: Pride Mafuratidze, Onisimo Mutanga, Mhosisi Masocha, Timothy Dube, Mbulisi Sibanda
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W7084062627
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