The surface defect plaque of spherical fruit is an important criterion for grading and sorting. Given the problem of the shape and size distortion of the plaques in the image by machine vision online sorting, according to the principle of projective geometry and general imaging configuration, the general imaging equation and the universal imaging area algorithm for plaque were established, and the area distortion law of plaque image was analyzed. Based on these above, the relationship between the effective image region and the parameters of the imaging system was calculated by Matlab to meet the requirements of the specific sorting accuracy, and a simple method for defining the effective image region was proposed. Using hemispheric blocks and Egyptian sweet oranges as the test subjects, the area distortion of the defective plaques located in the demarcated area captured by industrial cameras were verified with NI Vision Assistant. Assuming the distortion threshold (A) was 20%, the minimum spherical center angle (λ0min) could be calculated and the result was 7932°, and then the effective image area was designated. The experiment showed that the distortion degree of the defect plaques in the delineated area on the five hemisphere blocks were all less than the setting threshold under the specific image conditions. Similarly, for the five Egyptian sweet orange samples, the proportion of defective plaques that satisfied the distortion requirement were orderly 97.38%, 97.33%, 94.22%, 96.44% and 97.78%. Thus it’s available that the predicted results based on the general equation of spherical fruit surface plaque imaging can be used for the effective region segmentation to realize the effective control of sorting accuracy. The sorting threshold judgment by determining the area of defective plaques in the effective image region was conducive to simplify image processing, which can provide theoretical guidance for the imaging parameter configuration of the spherical fruit imaging system.