使用UAV优化室内农场监测效率：在无GNSS的樱桃番茄温室中的产量估计
Optimizing Indoor Farm Monitoring Efficiency Using UAV: Yield Estimation in a GNSS-Denied Cherry Tomato Greenhouse
AI总结：
针对温室监测中UGV的局限性，本文开发了一种配备RGB-D相机、3D LiDAR和IMU的轻量级UAV，利用LiDAR-惯性里程计和3D多目标跟踪算法，在无GNSS环境中实现樱桃番茄数量与重量估计，实验验证了其高效性与潜力。
摘要：
随着农业劳动力减少和劳动力成本上升，机器人产量估计变得越来越重要。虽然无人地面车辆（UGV）通常用于室内农场监测，但它们在温室中的部署通常受到基础设施限制、传感器放置挑战和运营效率低下的制约。为了解决这些问题，我们开发了一种轻量级无人机（UAV），配备了RGB-D相机、3D LiDAR和IMU传感器。该UAV采用LiDAR-惯性里程计算法在无GNSS的环境中进行精确导航，并利用3D多目标跟踪算法来估计樱桃番茄的数量和重量。我们使用两个数据集评估该系统：一个来自采收行，另一个来自生长行。在采收行数据集中，所提出的系统在10.5秒内完成的13.2米飞行中实现了94.4%的计数准确率和87.5%的重量估计准确率。对于由被遮挡的未成熟果实组成的生长行数据集，我们定性分析了跟踪性能，并强调了在具有强遮挡的温室中改善感知的未来研究方向。我们的发现证明了UAV在商业温室中进行高效机器人产量估计的潜力。
英文摘要：
As the agricultural workforce declines and labor costs rise, robotic yield estimation has become increasingly important. While unmanned ground vehicles (UGVs) are commonly used for indoor farm monitoring, their deployment in greenhouses is often constrained by infrastructure limitations, sensor placement challenges, and operational inefficiencies. To address these issues, we develop a lightweight unmanned aerial vehicle (UAV) equipped with an RGB-D camera, a 3D LiDAR, and an IMU sensor. The UAV employs a LiDAR-inertial odometry algorithm for precise navigation in GNSS-denied environments and utilizes a 3D multi-object tracking algorithm to estimate the count and weight of cherry tomatoes. We evaluate the system using two dataset: one from a harvesting row and another from a growing row. In the harvesting-row dataset, the proposed system achieves 94.4\% counting accuracy and 87.5\% weight estimation accuracy within a 13.2-meter flight completed in 10.5 seconds. For the growing-row dataset, which consists of occluded unripened fruits, we qualitatively analyze tracking performance and highlight future research directions for improving perception in greenhouse with strong occlusions. Our findings demonstrate the potential of UAVs for efficient robotic yield estimation in commercial greenhouses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
