Abstract: Apple grading is one of the critical steps to maximize returns. Among them, the high-quality apples can be sold as fresh at the premium prices, while the lower-grade apples can be used in process applications, such as juice, fresh-cut, or sauce. The apple grading is often conducted indoors using large-scale commercial sorting systems. However, the diseased apples can be mixed with the healthy ones during post-harvest transportation and storage, potentially leading to cross-contamination and economic losses. Apple field sorting equipment can be expected to perform the real-time grading immediately after harvest. The external quality can be identified, such as the fruit size, color, and surface defects, in order to reduce the risk of cross-contamination. Precise apple conveying and rotation can be found in the grading systems using machine vision. However, the limited space and high throughput have confined to capture the complete surface image for each apple in the field. It is also required that the conveying system with a single-row separation, smooth conveying, and uniform rotation. In this study, a variable-pitch spiral roller conveyor was designed to integrate the fruits separation, conveying, and rotation. The kinematic simulations of the variable-pitch spiral roller were conducted using ADAMS software. An iterative approach was employed to monitor the force conditions during apple conveying. A systematic investigation was also made to explore the impact of the different spiral roller speeds on the vibration during apple conveying. The optimal speed range was determined to be 2-3 r/s. A color-based method was also developed to calculate the cumulative coloring rate of the apple surfaces. Six apples were selected with the fruit diameters of (94.0 ± 0.5) mm (large), (84.0 ± 0.5) mm (medium), and (74.0 ± 0.5) mm (small). Each apple surface was uniformly divided into six vertical regions, and then coated with the six watercolor paints (red, white, yellow, blue, green, and purple) to cover the original peel color. The images were captured during the spiral drum conveying using motion image acquisition. The HSV color segmentation and morphological denoising techniques were also employed to extract each color region. Subsequently, the unfolded 2D images of the apple surface were generated for further analysis. A surface coloring rate algorithm was developed to calculate the cumulative coverage rate. A comparison was also made on the area of each colored region in the moving image with the area in the unfolded reference image. This ratio was used to estimate the number of rotations each apple made in the field of the camera’s view. A systematic analysis was then implemented on the relationship between fruit size and rotational behavior. The results show that: 1) A single apple rotated 3 times and 2 times, respectively, when the conveyor shaft operated at the speeds of 2 and 3 r/s, respectively. The imaging system simultaneously captured the image information from 12 apples within 1 second. 2) The horizontal displacement distance for each apple rotation increased by 53%-63% when the spacing increased from 8 to 12 mm. Overall, the spiral drum conveyor reduced the image information leakage, fully meeting the requirements for the fruit separation, uniform rotation, and high-throughput grading under field conditions with limited space. This finding can also provide a strong reference to explore the motion mechanisms of the fruits on the spiral drums.