LLM-led vision-spectral fusion: A zero-shot approach to temporal fruit image classification

A zero-shot multimodal framework for temporal image classification is proposed, targeting automated fruit quality assessment. The approach leverages large language models for expert-level semantic description generation, which guides zero-shot object detection and segmentation through GLIP and SAM models. Visual features and spectral data are fused to capture both external appearance and internal biochemical properties of fruits. Experiments on the newly constructed Avocado Freshness Temporal-Spectral dataset-comprising daily synchronized images and spectral measurements across the full spoilage lifecycle-demonstrate reductions in mean squared error by up to 33 % and mean absolute error by up to 17 % compared to established baselines. These results validate the effectiveness and generalizability of the framework for temporal image analysis in smart agriculture and food quality monitoring.