This research addresses the automation of wood grain sensory evaluation and judgment rationale generation using a small dataset. Wood grain sensory evaluation involves assessing sensory elements such as color and pattern, which leads to significant variations in evaluator judgments. This challenge complicates the establishment of consistent evaluations, standardization of evaluations, and evaluator training. To address this issue, we construct a dataset using data collected from an expert wood grain evaluator and fine-tune a Vision-Language Model (VLM) to automate wood grain sensory evaluation and generate judgment rationales. However, existing VLMs are found to have insufficient capabilities in extracting local image features, which is crucial for this type of evaluation. To improve local feature extraction capabilities, we modify the architecture of the existing VLM. Specifically, we integrate features from the intermediate layers of a CLIP ViT-based model with those from a Convolutional Neural Network (CNN), creating a model that can capture both local and global image characteristics. By fine-tuning this model, we aim to enhance the accuracy of wood grain sensory evaluation and rationale generation.