Multispectral imagery provides spectral information beyond the visible range for vegetation monitoring, but it typically suffers from limited spatial resolution, which can obscure fine crop structures and affect vegetation-index (VI) estimates.} This study evaluates whether single-image super-resolution (SISR) methods developed for red, green, and blue (RGB) imagery can enhance the spatial resolution of red, green, and near-infrared (RGN) crop images while preserving spectral fidelity. Eight pretrained methods are applied directly to RGN images without fine-tuning: bicubic and nearest-neighbor interpolation, Real-ESRGAN, HAT, HiT-SR, SRFormer, ResShift, and StableSR. Reconstruction at $4\times$ and $16\times$ is evaluated for whole images and manually segmented leaf regions. Reconstruction quality is assessed using mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), spectral angle mapper (SAM), and erreur relative globale adimensionnelle de synthèse (ERGAS). Eight VIs, computed from reference and super-resolved images, are evaluated using MAE, PSNR, and SSIM. Transformer-based methods \modification{significantly} outperform GAN- and diffusion-based approaches in both evaluation settings \modification{($p < 0.001$)}, with \modification{HAT, HiT-SR, and SRFormer forming the highest-performing statistical tier}. These findings indicate that RGB-trained SISR can enhance spatial detail in RGN crop imagery while maintaining spectral consistency and VI reliability for high-resolution crop phenotyping.

翻译：暂无翻译