This paper presents an experimental comparison between model-based and model-free trajectory-tracking control strategies applied to an omnidirectional mobile robot platform. Classical model-based controllers, including Proportional-Integral-Derivative (PID), Generic Model Control (GMC), and Sliding Mode Control (SMC), are evaluated against a Model-Free Control (MFC) strategy based on an intelligent Proportional–Integral–Derivative (iPID) regulator. An empirical integrator-with-delay model is identified from experimental data and used to design model-based controllers. All strategies were implemented on the Festo Robotino platform under comparable operating conditions and evaluated using circular, lemniscate, and square trajectories. Controller performance is assessed using the Integral of Squared Error (ISE), the settling time ($T_s$), and the control effort quantified by the Integral of Squared Control Output (ISCO). Experimental results show that the model-free controller provides the best overall tracking performance, achieving tracking error reductions of approximately $25\%$ to $90\%$ compared with the evaluated model-based controllers, while maintaining a competitive control effort. The study provides a unified experimental benchmark comparing model-based and model-free control paradigms on the Robotino platform, highlighting the practical advantages of model-free control for robotic systems affected by uncertainties and nonlinearities.

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× and 16× 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, and erreur relative globale adimensionnelle de synthése. Eight VIs, computed from reference and super‐resolved images, are evaluated using MAE, PSNR, and SSIM. Transformer‐based methods significantly outperform GAN‐ and diffusion‐based approaches in both evaluation settings ( p < 0.001), with 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.