This study aims to develop a crop classification method by integrating vehicle images with HLS time-series remote sensing data. The goal is to enhance classification efficiency and accuracy, addressing the limitations of traditional methods such as low efficiency in ground sample collection and insufficient utilization of remote sensing phenological features. A vehicle-mounted camera system was deployed to collect manually annotated crop samples along road networks, combined with HLS time-series data from 2023 and 2024. Gaussian filtering was applied to reconstruct the time-series imagery, and the Random Forest classification method was employed to classify three major crops: rice, maize, and soybean. Results demonstrated significant differences in the characteristics of rice, maize, and soybean in the HLS time-series data. Among these crops, rice achieved the highest classification accuracy, with both producer's and user's accuracy exceeding 90%, whereas maize and soybean had lower accuracies (74%-85%) due to their similar phenological characteristics. The overall classification accuracy in the validation area was 89%. The rice in the verification area is mainly distributed in the southeast region of the county, while corn and soybeans are concentrated in the northwest region, and their distribution characteristics are clear. The integration of vehicle images and HLS time-series data proves effective for crop classification, with the Random Forest model demonstrating superior performance in handling high-dimensional features and sample imbalance. However, challenges remain in fragmented farmland and cloud-covered areas. Future improvements should focus on incorporating multi-source data to address cloud contamination and mixed-pixel effects in fragmented areas, while expanding crop categories to enhance model generalizability for broader agricultural applications.