Robots in tomato greenhouses need to perceive the plant and plant parts accurately to automate monitoring, harvesting, and de-leafing tasks. Existing perception systems struggle with the high levels of occlusion in plants and often result in poor perception accuracy. One reason for this is because they use fixed cameras or predefined camera movements. Next-best-view (NBV) planning presents an alternate approach, in which the camera viewpoints are reasoned and strategically planned such that the perception accuracy is improved. However, existing NBV-planning algorithms are agnostic to the task-at-hand and give equal importance to all the plant parts. This strategy is inefficient for greenhouse tasks that require targeted perception of specific plant parts, such as the perception of leaf nodes for de-leafing. To improve targeted perception in complex greenhouse environments, NBV planning algorithms need an attention mechanism to focus on the task-relevant plant parts. In this paper, the role of attention in improving targeted perception using an attention-driven NBV planning strategy was investigated. Through simulation experiments using plants with high levels of occlusion and structural complexity, it was shown that focusing attention on task-relevant plant parts can significantly improve the speed and accuracy of 3D reconstruction. Further, with real-world experiments, it was shown that these benefits extend to complex greenhouse conditions with natural variation and occlusion, natural illumination, sensor noise, and uncertainty in camera poses. The results clearly indicate that using attention-driven NBV planning in greenhouses can significantly improve the efficiency of perception and enhance the performance of robotic systems in greenhouse crop production.

翻译：番茄温室中的机器人需要精确感知植株及其部位，以实现监测、采摘和去叶任务的自动化。现有感知系统因植株高度遮挡问题而难以应对，常导致感知精度不足。其原因之一是这些系统采用固定摄像头或预定义相机运动轨迹。最优视角规划提供了一种替代方案：通过推理和策略性规划相机视点来提升感知精度。然而，现有最优视角规划算法对具体任务无差别对待，对所有植株部位赋予同等重要性。这种策略对于需要针对性感知特定植株部位（如去叶任务中的叶节点感知）的温室任务而言效率低下。为提升复杂温室环境中的目标感知能力，最优视角规划算法需要引入注意力机制以聚焦任务相关植株部位。本文研究了注意力机制通过注意力驱动最优视角规划策略提升目标感知能力的作用。通过对具有高度遮挡和结构复杂性的植株进行仿真实验，证明聚焦任务相关植株部位能显著提升三维重建的速度与精度。此外，通过真实环境实验表明，这些优势可延伸至具有自然变异与遮挡、自然光照、传感器噪声及相机位姿不确定性的复杂温室条件。结果明确显示，在温室中采用注意力驱动最优视角规划能显著提升感知效率，并增强机器人在温室作物生产中的作业性能。