Automated potato harvesting requires vision systems meeting stringent operational constraints: near-zero potato misclassification (PMR <1%) and high impurity detection rate (IDR >60%). Although YOLOX supports real-time processing, it exhibits critical performance limitations for small object detection. This paper introduces a unified optimization strategy combining training-level modifications (P3 feature enhancement, SimOTA parameter optimization, and size-aware loss weighting) with inference-level threshold optimization. Experimental results based on 10,000 images containing 232,000 annotations demonstrate that all four evaluated approaches achieved the defined operational constraints. SimOTA emerged as the optimal configuration, delivering the highest small object recall (18.18%) while maintaining PMR of 0.06% and IDR of 95.05%. P3 feature enhancement achieved the lowest PMR (0.05%), SALW provided balanced performance (PMR 0.06%, IDR 96.11%), and the P3+SimOTA combination failed critically (PMR 9.90%), revealing fundamental incompatibility between optimization components. All successful configurations exceeded real-time processing requirements (45–61 fps), confirming suitability for deployment in resource-constrained agricultural automation systems.

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