Automated and selective harvesting of fruits has become an important area of research, particularly due to challenges such as high costs and a shortage of seasonal labor in advanced economies. This paper focuses on 6D pose estimation of strawberries using purely synthetic data generated through a procedural pipeline for photorealistic rendering. We employ the YOLOX-6D-Pose algorithm, a single-shot approach that leverages the YOLOX backbone, known for its balance between speed and accuracy, and its support for edge inference. To address the lacking availability of training data, we introduce a robust and flexible pipeline for generating synthetic strawberry data from various 3D models via a procedural Blender pipeline, where we focus on enhancing the realism of the synthesized data in comparison to previous work to make it a valuable resource for training pose estimation algorithms. Quantitative evaluations indicate that our models achieve comparable accuracy on both the NVIDIA RTX 3090 and Jetson Orin Nano across several ADD-S metrics, with the RTX 3090 demonstrating superior processing speed. However, the Jetson Orin Nano is particularly suited for resource-constrained environments, making it an excellent choice for deployment in agricultural robotics. Qualitative assessments further confirm the model's performance, demonstrating its capability to accurately infer the poses of ripe and partially ripe strawberries, while facing challenges in detecting unripe specimens. This suggests opportunities for future improvements, especially in enhancing detection capabilities for unripe strawberries (if desired) by exploring variations in color. Furthermore, the methodology presented could be adapted easily for other fruits such as apples, peaches, and plums, thereby expanding its applicability and impact in the field of agricultural automation.

翻译：水果的自动化选择性采摘已成为重要研究领域，尤其在发达经济体面临高成本和季节性劳动力短缺等挑战的背景下。本文聚焦于利用通过程序化流程生成的光学真实感渲染纯合成数据，进行草莓的六维姿态估计。我们采用YOLOX-6D-Pose算法——一种基于YOLOX骨干网络的单次检测方法，该骨干网络以速度与精度的平衡及边缘推理支持而著称。针对训练数据稀缺的问题，我们提出通过程序化Blender流程从多种三维模型生成合成草莓数据的鲁棒灵活流程，重点提升合成数据相较于先前工作的真实感，使其成为训练姿态估计算法的有效资源。定量评估表明，我们的模型在NVIDIA RTX 3090和Jetson Orin Nano平台上，于多项ADD-S指标上均达到可比精度，其中RTX 3090展现出更优处理速度。而Jetson Orin Nano特别适合资源受限环境，是农业机器人部署的理想选择。定性评估进一步验证了模型性能，证明其能准确推断成熟与半熟草莓的姿态，但在检测未成熟样本时存在挑战。这为未来改进提供了方向，特别是通过探索颜色变化来增强未成熟草莓的检测能力（如需要）。此外，本方法可轻松适配苹果、桃、李等其他水果，从而拓展其在农业自动化领域的适用性与影响力。