Color-accurate digital imaging is critical for agricultural phenotyping, but the scientific literature predominantly assumes the availability of linear RAW sensor data. In some commercial workflows, only standard 8-bit sRGB JPEG images are available, which poses a significant challenge due to their non-linear encoding and information loss from in-camera processing. This paper presents a robust color correction pipeline designed specifically for such non-linear images captured in uncontrolled outdoor environments. Our core contribution is a novel, per-patch adaptive weighting scheme for least-squares color correction. Instead of deriving a single global correction, our method generates a unique transformation for each patch on an in-frame ColorChecker. This is achieved through a leave-one-out approach where, for each target patch, a model is trained on the remaining 23 patches. Crucially, this training is guided by a weighting matrix customized for the target patch. This adaptive process allows a simple linear model to outperform more complex polynomials. Through systematic evaluation on an unseen test set, we demonstrate this method reduces the mean color error ΔE00 from 11.23 to 3.79, providing a practical and effective solution for real-world agricultural imaging.