发表机构
University of California Davis（加州大学戴维斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对AI豇豆花荚检测在不同基因型环境下泛化差的问题，发现优化的HDR合成数据仅需少量真实图像即可达到真实数据基线性能，证实合成数据可克服该检测的泛化极限。
AI 中文摘要
高通量表型分析需要能在基因型、地点及生长季间泛化的AI计算机视觉模型，但此类模型在新条件下常出现精度下降。为育种项目中每一种基因型-环境（G×E）组合标注真实图像的成本过高，本研究量化了G×E变化对加州两个地点、两个生长季下基于AI的豇豆花与荚果检测的影响：未见过的变化下，花检测的mAP@50从76.3%降至低至50.6%，荚果检测对变化更敏感；特征空间与图像质量诊断证实，精度损失与可测量的分布变化相关。由于仅用真实数据缩小该差距不现实，本研究测试了由程序化3D豇豆模型生成的合成图像能否替代标注负担：仅用合成监督的性能较预训练有所提升，但受限于由相机成像而非场景内容导致的领域差距；一种基于Wasserstein距离、针对实测真实图像统计量优化的感知领域差距的相机真实感增强策略，缩小了该差距；线性HDR表示相比8位表示，将更小的实测差距转化为更大的检测增益；优化后的HDR合成数据仅搭配5张真实图像，即可达到或超过真实数据基线的空间泛化性能，荚果检测在低样本数时受益最大，时间变化下增益较温和。结果表明，合成数据可克服基于AI的花与荚果检测的泛化极限，但前提是需测量并优化领域差距，而非忽略它。
英文摘要
High-throughput phenotyping requires AI-enabled computer vision models that generalize across genotypes, locations, and growing seasons, yet such models often lose accuracy under new conditions. Annotating real imagery for every genotype-by-environment (G x E) combination a breeding program encounters is prohibitively expensive. We quantify how G x E shifts affect AI-based detection of cowpea flowers and pods across two California locations and two growing seasons. Flower detection mAP@50 fell from 76.3% to as low as 50.6% under unseen shifts, and pod detection was more sensitive. Feature-space and image-quality diagnostics confirmed these losses track measurable distributional shifts. Because closing this gap with real data alone is not practical, we test whether synthetic imagery, rendered from a procedural 3D cowpea model, can substitute for that annotation burden. Synthetic supervision alone improved over pretraining but remained limited by a domain gap driven by camera image formation, not scene content. A domain-gap-aware camera-realism augmentation strategy, optimized against measured real-image statistics via Wasserstein distance, narrowed this gap, and a linear HDR representation converted a smaller measured gap into a larger detection gain than an 8-bit representation. Optimized HDR synthetic data combined with as few as five real images matched or exceeded the real-data baseline for spatial generalization, and pod detection benefited most at the lowest shot counts, with more modest gains under temporal shift. These results show that synthetic data can overcome the generalization limits of AI-based flower and pod detection, but only when the domain gap is measured and optimized rather than assumed away.
