Semantic maps are fundamental for robotics tasks such as navigation and manipulation. They also enable yield prediction and phenotyping in agricultural settings. In this paper, we introduce an efficient and scalable approach for active semantic mapping in horticultural environments, employing a mobile robot manipulator equipped with an RGB-D camera. Our method leverages probabilistic semantic maps to detect semantic targets, generate candidate viewpoints, and compute the corresponding information gain. We present an efficient ray-casting strategy and a novel information utility function that accounts for both semantics and occlusions. The proposed approach reduces total runtime by 8 % compared to previous baselines. Furthermore, our information metric surpasses other metrics in reducing multiclass entropy and improving surface coverage, particularly in the presence of segmentation noise. Real-world experiments validate our method's effectiveness but also reveal challenges such as depth sensor noise and varying environmental conditions, requiring further research. https://github.com/jrcuaranv/nbv_planning.

Due to labor shortages in specialty crop industries, a need for robotic automation to increase agricultural efficiency and productivity has arisen. Previous manipulation systems harvest well in uncluttered and structured environments. High tunnel environments are more compact and cluttered in nature, requiring a rethinking of the large form factor systems and grippers. We propose a novel co-designed framework incorporating a global detection camera and a local eye-in-hand camera that demonstrates precise localization of small fruits via closed-loop visual feedback and reliable error handling. Field experiments in high tunnels show that our system can reach 85.0% of cherry tomato fruit in 10.98s on average.