Monitoring vegetation in arid rangelands is challenging because exposed soil, dry litter, shadows, and sparse canopy structure reduce the stability of standard segmentation workflows. This article presents a Google Gemini generative-AI-assisted methodology for producing annotation-ready vegetation priors from visually heterogeneous rangeland imagery. The workflow was applied to RGB images of indigenous forage species from Marsabit County, Kenya, where semantic reconstruction transformed noisy field scenes into structured representations for staged scene parsing, mask refinement, and final object-mask generation. Paired spectral analysis across 98 original and generated image pairs showed that reconstruction produced a measurable spectral-domain shift rather than simple background-variance reduction. Background coefficient of variation increased by 125.4 %, background Shannon entropy by 49.8 %, and target-vegetation Coefficient of Variation (CoV) by 11.4 %, indicating texture redistribution after reconstruction. Jeffries-Matusita separability changed only slightly, with a global shift of-0.8 %, suggesting that vegetation background distinction remained close to saturation. The reconstruction-enhanced priors used for automated annotation and evaluated using YOLOv8, YOLOv11, and RF-DETR. protocol converts noisy, spectrally heterogeneous rangeland imagery into segmentation-ready training data for ecological computer vision, although broader geographic generalization quires validation on independent dryland datasets.