Real-time hyperspectral decision support for precision herbicide management in Maize: Optimizing herbicide efficacy and plant response

Hyperspectral VNIR imaging (400–1000 nm) offers a swift, non-invasive method for identifying early herbicide-induced stress in maize and its accompanying weed flora. Unlike traditional visual scoring and biomass measurements, this technology captures subtle changes in pigment content, water status, and canopy structure with high precision and accuracy. The present study employed hyperspectral vegetation indices and multivariate analysis to identify spectral responses to sequential pre- and post-emergence herbicide combinations, measure the dynamics of chlorophyll, carotenoid, and anthocyanin-related pigments under herbicide stress, and differentiate treatment efficacy patterns to enhance precision weed management. Hyperspectral reflectance data collected before and after herbicide application were used to calculate indices, including NDVI, CIgreen, CIred-edge, NPQI, CRI1, CRI2, ARI1, and ARI2. This was shown by a drop in CIred-edge (26.2%) and CIgreen (8.8%), and a considerable increase in NPQI (+68.4%), CRI2 (+63.4%), and ARI1 (+824.1%) within four days of application, indicating that pigments break down quickly and weed species are sensitive. In contrast, combinations based on halosulfuron methyl showed very little spectral divergence and mostly resembled the weedy check because Cyperus spp., the main target of the herbicide, was not present. Principal Component Analysis showed that the first two components explained 78.5% of the total variance (PC1:56.9%; PC2:21.6%), successfully distinguished tembotrione-based combinations from other regimens, whereas hierarchical clustering categorised treatments based on their temporal spectral response patterns. These results show that hyperspectral imaging and multivariate analysis can provide an objective and early indication of herbicide efficacy. This study presents a real-time decision-support framework that improves precision herbicide management, reduces dependence on subjective evaluations, and fosters more sustainable maize production methodologies.