Searching and detecting the task-relevant parts of plants is important to automate harvesting and de-leafing of tomato plants using robots. This is challenging due to high levels of occlusion in tomato plants. Active vision is a promising approach in which the robot strategically plans its camera viewpoints to overcome occlusion and improve perception accuracy. However, current active-vision algorithms cannot differentiate between relevant and irrelevant plant parts and spend time on perceiving irrelevant plant parts. This work proposed a semantics-aware active-vision strategy that uses semantic information to identify the relevant plant parts and prioritise them during view planning. The proposed strategy was evaluated on the task of searching and detecting the relevant plant parts using simulation and real-world experiments. In simulation experiments, the semantics-aware strategy proposed could search and detect 81.8% of the relevant plant parts using nine viewpoints. It was significantly faster and detected more plant parts than predefined, random, and volumetric active-vision strategies that do not use semantic information. The strategy proposed was also robust to uncertainty in plant and plant-part positions, plant complexity, and different viewpoint-sampling strategies. In real-world experiments, the semantics-aware strategy could search and detect 82.7% of the relevant plant parts using seven viewpoints, under complex greenhouse conditions with natural variation and occlusion, natural illumination, sensor noise, and uncertainty in camera poses. The results of this work clearly indicate the advantage of using semantics-aware active vision for targeted perception of plant parts and its applicability in the real world. It can significantly improve the efficiency of automated harvesting and de-leafing in tomato crop production.

翻译：在番茄植株自动化采收与去叶作业中，搜索并检测任务相关的植物部位至关重要。由于番茄植株存在高度遮挡，该任务极具挑战性。主动视觉是一种前景广阔的方法，机器人通过策略性地规划相机视点以克服遮挡并提升感知精度。然而，现有主动视觉算法无法区分相关与无关植物部位，导致大量时间耗费在对无关部位的感知上。本研究提出一种语义感知的主动视觉策略，该策略利用语义信息识别相关植物部位并在视点规划中予以优先考虑。通过仿真与真实环境实验，对所提策略在搜索与检测相关植物部位任务中的性能进行了评估。仿真实验中，所提出的语义感知策略仅需九个视点即可搜索并检测到81.8%的相关植物部位。相较于未使用语义信息的预定义、随机及体素化主动视觉策略，该策略速度显著更快且检测到的植物部位更多。该策略对植株及部位位置不确定性、植株复杂度以及不同视点采样策略均表现出良好鲁棒性。在真实环境实验中，面对温室复杂条件下存在的自然形态变异、遮挡、自然光照、传感器噪声及相机位姿不确定性，语义感知策略仅用七个视点即可搜索并检测到82.7%的相关植物部位。本研究结果清晰表明，语义感知主动视觉在植物部位目标感知方面具有显著优势，且具备实际应用可行性。该技术可显著提升番茄作物生产中自动化采收与去叶作业的效率。