用视觉技术在线估算遮挡草莓质量，误差低于11%。
Online Estimation of Table-Top Grown Strawberry Mass in Field Conditions with Occlusions
- 结合深度相机与AI分割，补全遮挡区域并校正姿态。
- 遮挡草莓质量估计误差为10.47%，孤立草莓误差8.11%。
- 适合智能采摘与田间产量监测，抗遮挡能力强。
由于频繁遮挡和姿态变化，田间环境下对架栽草莓的精确质量估计算法仍具挑战性。本研究提出一种基于视觉的端到端流程，融合RGB-D传感与深度学习，实现非破坏性、实时且在线的质量估计。方法采用YOLOv8-Seg进行实例分割，利用循环一致性生成对抗网络（CycleGAN）完成遮挡区域重建，并通过倾斜角校正优化前视投影面积计算；随后使用多项式回归模型将几何特征映射至质量。实验表明，在无遮挡情况下平均误差为8.11%，遮挡情况下为10.47%。相较大型掩码修复模型LaMa，CycleGAN在遮挡恢复中表现更优：像素面积比（PAR）均值分别为0.978（优于1.112），交并比（IoU）在[0.9-1]区间达92.3%（远超LaMa的47.7%）。该方法克服了传统方法的关键缺陷，为具有复杂遮挡模式的自动化采收与产量监测提供了鲁棒解决方案。
原文摘要 · Abstract (English)
Accurate mass estimation of table-top grown strawberries under field conditions remains challenging due to frequent occlusions and pose variations. This study proposes a vision-based pipeline integrating RGB-D sensing and deep learning to enable non-destructive, real-time and online mass estimation. The method employed YOLOv8-Seg for instance segmentation, Cycle-consistent generative adversarial network (CycleGAN) for occluded region completion, and tilt-angle correction to refine frontal projection area calculations. A polynomial regression model then mapped the geometric features to mass. Experiments demonstrated mean mass estimation errors of 8.11% for isolated strawberries and 10.47% for occluded cases. CycleGAN outperformed large mask inpainting (LaMa) model in occlusion recovery, achieving superior pixel area ratios (PAR) (mean: 0.978 vs. 1.112) and higher intersection over union (IoU) scores (92.3% vs. 47.7% in the [0.9-1] range). This approach addresses critical limitations of traditional methods, offering a robust solution for automated harvesting and yield monitoring with complex occlusion patterns.
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