Abstract: A winnowing machine has been used to screen the residual film and impurities for resource utilization as post cotton harvesting. However, the manual cleaning hinders its further development after screen hole clogging, due mainly to the high labor costs and low efficiency. In this study, a multi-region dual-column unclogging system was proposed to realize the automatic cleaning operations. Firstly, the YOLOv8s model was selected for the identification after the comparative experiments on the YOLO series. Then, the pruning experiments were conducted to determine a pruning rate of 40% followed by 200 epochs of fine-tuning. The YOLOv8s-prune model was also established after verification. Finally, the identification performance of the YOLOv8s-prune was evaluated to compare with the various object detection. The results showed that the YOLOv8s model achieved the optimal performance on the validation set: precision (P) of 99.4%, recall (R) of 99.2%, mAP_@0.5 of 99.5%, mAP_@0.5-0.95 of 94.7%, and a model size of 21.4 MB. After that, the YOLOv8s-prune model was achieved in a precision (P) of 99.3%, recall (R) of 99%, mAP_@0.5 of 99.5%, mAP_@0.5-0.95 of 92.1%, and a model size of 10.9 MB. There was a decrease of 0.1, 0.2, 0, and 2.6 percentage points, respectively, while the model size was reduced by 50%, compared with the YOLOv8s before optimization. The YOLOv8s-prune model significantly reduced the number of parameters, computational load, and model size, compared with the rest object detection. On the test set, it was achieved in a precision (P) of 98.9%, a recall (R) of 99%, and mAP_@0.5 of 99.4%. The coordinate information was also identified by YOLOv8s-prune. An identification control system was constructed to take a microcontroller (STM32F103C8T6) as the controller. The coordinate information was also divided into four intervals corresponding to four nozzles. The motion module traveled 360 mm per cycle was realized in the queue-based positioning between the nozzles and screen holes. Positional accuracy compensation was implemented for the motion module, according to the difference between the identified X-axis coordinate value of the leftmost screen hole and its actual coordinate value. The performance tests showed that the average recognition rate of the model was 98.3% within 0-10 min, 95.4% within 10-20 min, and 92.7% within 20-30 min, respectively, during sieving operations. The average recognition rate also exceeded 94% for the screen holes. The queue positioning test indicated that the absolute positioning deviation of the motion module ranged from a maximum of 25.6 mm to a minimum of 7 mm, with a qualification rate exceeding 94%, thus fully meeting the requirements for the cyclic operations. Orthogonal experiments on the whole machine revealed that the influencing factors on the unblocking rate were ranked in the descending order of: unblocking wind speed, operating distance, and unblocking angle. The maximum unblocking rate of 93.535% was achieved under the optimal conditions. Furthermore, the optimal combination of the rounded parameter was: unblocking wind speed of 5 m/s, unblocking angle of 11°, and operating distance of 120 mm. The average unblocking rate was 90.56% with an error of 2.975 percentage points, compared with the theoretical prediction, thus meeting the operational requirements of the film residue winnower. The findings can also provide a strong reference to develop the automatic unblocking for the film residue winnowers.