Simulation-Based Evaluation of Vision-Based Adaptive Conveyor Speed Control Using Reel-Synchronous Onion Counting in a Self-Propelled Onion Collector

The stream of onions entering a self-propelled onion collector varies with field conditions, feeding density, and the transient lifting behavior of the onion–soil mass, whereas the collection conveyor is conventionally operated at a fixed speed, wasting hydraulic energy. This study proposes and evaluates, through field-calibrated simulation, a vision-based feedforward conveyor speed control framework that couples reel-synchronous onion counting with variable-displacement pump control. To mitigate the periodic occlusion caused by the compact dual-conveyor structure, a frame-selection method synchronized with the detected conveyor position was implemented, and a YOLOv8n detector was trained and evaluated on 314 images extracted from 32 indoor and field source videos partitioned at the source-video level. On the independent test subset, the model achieved precision, recall, and mAP@0.5 of 0.952, 0.944, and 0.959, and the proposed counting method maintained count recovery ratios above 95% across all engine speeds in 45 independent field trials, outperforming fixed-period sampling and tracking-based baselines by 6.6 and 3.8 percentage points, respectively. An AMESim model of the fixed-displacement hydraulic system was calibrated and shown to be consistent with field measurements and was then used to evaluate the variable-displacement configuration. Net conveyor-related fuel savings (engine no-load consumption subtracted) were estimated at 14.3–18.7% under the field-identified constant transmission efficiency, with conservative estimates of 11.5–16.9% when partial-displacement efficiency losses were accounted for through an anchored loss model. These results demonstrate the feasibility of vision-based feedforward conveyor speed control and its potential for energy savings in bulb-crop collection.