The advancement of agricultural robotics holds immense promise for transforming fruit harvesting practices, particularly within the apple industry. The accurate detection and localization of fruits are pivotal for the successful implementation of robotic harvesting systems. In this paper, we propose a novel approach to apple detection and position estimation utilizing an object detection model, YOLOv5. Our primary objective is to develop a robust system capable of identifying apples in complex orchard environments and providing precise location information. To achieve this, we curated an autonomously labeled dataset comprising diverse apple tree images, which was utilized for both training and evaluation purposes. Through rigorous experimentation, we compared the performance of our YOLOv5-based system with other popular object detection models, including SSD. Our results demonstrate that the YOLOv5 model outperforms its counterparts, achieving an impressive apple detection accuracy of approximately 85%. We believe that our proposed system's accurate apple detection and position estimation capabilities represent a significant advancement in agricultural robotics, laying the groundwork for more efficient and sustainable fruit harvesting practices.

翻译：农业机器人的进步为变革水果采摘方式带来了巨大潜力，尤其在苹果产业中，果实的精准检测与定位对于实现机器人采摘系统至关重要。本文提出了一种基于目标检测模型YOLOv5的苹果检测与位置估计新方法。主要目标是开发一个能够在复杂果园环境中识别苹果并提供精确位置信息的鲁棒系统。为此，我们构建了一个包含多样化苹果树图像的自主标注数据集，用于训练与评估。通过严格的实验，我们将基于YOLOv5的系统与其他主流目标检测模型（包括SSD）进行了性能对比。结果表明，YOLOv5模型优于其他模型，苹果检测精度达到约85%。我们认为，本文所提出的系统在苹果精准检测与位置估计方面的能力代表了农业机器人的重要进展，为更高效、可持续的水果采摘实践奠定了基础。