This study explores Few-Shot Learning (FSL) to improve a pre-trained Transformer model for classifying avocado ripeness using Near-Infrared (NIR) spectral data from a portable, low-cost scanner. The goal is to allow users in the field to customise the model with little local data, decreasing reliance on centralised retraining while keeping predictive accuracy intact. A dataset was created with spectral scans from the AS7265x sensor, capturing 18 wavelengths for each sample. The population consists of Buccaneer avocado samples at three ripeness stages: raw, aged, and ripe. Subsets of different sizes were extracted for simulation to represent various k-shot support scenarios, from 2% to 50% of the total labelled data per class. A few-shot Transformer architecture classified samples in episodic learning conditions. We assessed performance through average F1-score, ROC AUC, and confusion matrices using 5-fold cross-validation. Findings show that with as little as 20% labelled support data per class, the model achieves over 80% F1-score, demonstrating a clear performance gain through minimal shot expansion. Classification accuracy keeps improving until it levels off around 40–50% shot ratios. This study advances embedded agricultural AI by demonstrating the effective use of few-shot adaptation in portable NIR scanners for localised model improvement. Limitations involve possible sensitivity to class imbalance and noise in low-shot scenarios, necessitating strong sampling and calibration.