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• Apple image correction based on the adaptive Gamma method. • Using the basic image processing method to extract the complete fruit regions. • Obtaining the near-large fruit by the optimized iterative open operation method. It is appropriate for near-large fruit to be the first target of single-arm harvesting robot based on machine vision in each operation. The work introduced a method to obtain the near-large fruit from apple image in orchard. Firstly, the R-channel and G-channel images of orchard apple RGB (Red, Green, Blue) images were operated by the adaptive Gamma correction method to overcome the influence of natural illumination on subsequent image segmentation. After combination, the treatment process was formed by the basic image processing method such as corrosion, expansion, hole filling, and fixed threshold segmentation to obtain a complete, clean fruit area in the collected apple image. Finally, the near-large fruit was obtained from apple image of orchard based on the optimized iterative open operation. To verify the designed method, 30 apple images were selected according to strong to low light conditions from the captured images. Experimental results showed that the apple image after the adaptive Gamma correction was segmented to obtain the cleaner fruit region. The proposed method was used to obtain the correct near-large fruit with nearly real area. The optimized iterative open operation reduced the execution time of entire algorithm, with average and maximum reduction ratios of 70 and 84%, respectively. [ABSTRACT FROM AUTHOR]