Traditional metrics such as Fréchet Inception Distance (FID) and Inception Score (IS) fail to capture fine-grained semantic attributes like fruit maturity. To address this gap, we propose BananaSense—a visual analytics system for maturity-aware banana image generation with applications in cold chain monitoring. Based on a Latent Diffusion Model (LDM), our system synthesizes bananas with target ripeness levels to infer real-time maturity changes during transportation where physical inspection is infeasible. We construct a labeled dataset by integrating public banana images with expert sensory data. For evaluation, BananaSense computes cosine similarities between generated images’ color vectors (RGB/HSV/LAB) and predefined maturity references, assigning the closest category as the predicted maturity. By bridging synthetic data generation and cold chain logistics, BananaSense delivers an interpretable solution for non-invasive ripeness tracking in agricultural supply chains.