Owing to the requirements of a high yield and high-quality tomatoes, tomato grading is important—particularly for fruit morphology. Machine vision provides a fast and nondestructive manner to address this demand, and accuracy has become the focus of attention. In this study, the gamma correction method was used for preprocessing to enhance the surface reflection of tomatoes, and Otsu’s method was used to segment the A scalar diagram under the LAB color model. On this basis, two levels of exploration were conducted. First, new evaluation indices were proposed for different views. For the top view, two shape-evaluation indices were established: the area ratio of the maximum inscribed circle to the maximum circumscribed circle and the dispersion of the contour centroid distance (extreme value and coefficient of variation). For the side view, the difference between the maximum and minimum centroid distances in the contour was established as a shape index. Compared to with commonly used shape features, the shape-detection accuracy of the proposed indicators proposed was > 7% higher. Second, an evaluation method based on multi-view fusion was developed by combining the advantage indices for different views. The detection accuracy was 96%, which was 9.40–9.70% higher than those for the top and side views alone. The proposed evaluation method combining top views (dispersion of centroid distance) with side views (difference between maximum and minimum centroid distances) is effective for classifying tomatoes.