Completing a series of commercialization processing steps such as cleaning, waxing, and grading of citrus on the same production line is conducive to reducing fruit damage and improving fruit quality. However, the presence of diseased and damaged citrus can easily cause cross-contamination and pollution to the subsequent production line. Therefore, it is necessary to remove diseased and damaged citrus at the feeding section of the production line. For this reason, a disease-damaged citrus preliminary screening system was developed based on deep learning and Delta robots. Firstly, through comparative experiments of different detection models, the YOLO v7 model with the highest accuracy was selected, and combined with the DeepSORT tracking algorithm to achieve rapid and precise tracking and detection of citrus on the production line. Secondly, an optimized Delta robot door-shaped trajectory was proposed, and the precise control strategy of the stepping motor was calculated based on the interpolation method. Finally, a prototype of a screening device with fast positioning and grasping capabilities was built and integrated into the production line. The experimental results showed that the F1-score of the YOLO v7 model was 90%, which was 2 and 4 percentage points higher than that of the YOLO v5 and SSD networks, respectively. The Delta robot designed had high positioning accuracy, with an average positioning error of 1.5mm for the same point, which met the precision requirements for grasping. The average success rate of screening out diseased and damaged oranges could reach 83.25%. Therefore, the equipment proposed had excellent automatic screening capabilities on the citrus sorting production line, which can effectively reduce the cross-contamination of diseased and damaged citrus and pollution to the production line, thus ensuring the normal operation of the citrus production line.