Modern robotics has enabled the advancement in yield estimation for precision agriculture. However, when applied to the olive industry, the high variation of olive colors and their similarity to the background leaf canopy presents a challenge. Labeling several thousands of very dense olive grove images for segmentation is a labor-intensive task. This paper presents a novel approach to detecting olives without the need to manually label data. In this work, we present the world's first olive detection dataset comprised of synthetic and real olive tree images. This is accomplished by generating an auto-labeled photorealistic 3D model of an olive tree. Its geometry is then simplified for lightweight rendering purposes. In addition, experiments are conducted with a mix of synthetically generated and real images, yielding an improvement of up to 66% compared to when only using a small sample of real data. When access to real, human-labeled data is limited, a combination of mostly synthetic data and a small amount of real data can enhance olive detection.

翻译：现代机器人技术推动了精准农业中产量估计的进步。然而，当应用于橄榄产业时，橄榄颜色高度变异及其与背景叶冠的相似性带来了挑战。为分割数万张高密度橄榄园图像进行人工标注，是一项劳动密集型任务。本文提出了一种无需手动标注数据的新型橄榄检测方法。在这项工作中，我们展示了全球首个包含合成与真实橄榄树图像的橄榄检测数据集。这是通过生成自动标注的照片级逼真橄榄树3D模型实现的，随后对其几何结构进行简化以实现轻量化渲染。此外，通过混合使用合成生成图像与真实图像开展的实验表明，与仅使用少量真实数据样本相比，检测性能提升高达66%。当真实人工标注数据有限时，将大多数合成数据与少量真实数据相结合，可增强橄榄检测效果。