无人机+多传感器实现无卫星信号温室番茄高效估产。
Optimizing Indoor Farm Monitoring Efficiency Using UAV: Yield Estimation in a GNSS-Denied Cherry Tomato Greenhouse
- 用无人机搭载深度相机与激光雷达，实现无信号环境精准定位。
- 采摘行番茄计数准确率94.4%，重量估计准确率87.5%，10秒完成13米飞行。
- 适合需要高效率、低人工的智能温室场景，尤其适合遮挡严重区域。
随着农业劳动力减少和人工成本上升，机器人化产量估计变得愈发重要。尽管无人地面车（UGVs）常用于温室监测，但其部署受限于基础设施、传感器布置及运行效率。为此，我们开发了一款轻量级无人机，配备RGB-D相机、3D LiDAR和IMU传感器。该无人机采用激光雷达-惯性里程计算法，在无GNSS环境下实现精准导航，并通过3D多目标跟踪算法估算樱桃番茄的数量与重量。我们在两个数据集上评估系统：一个来自采收行，另一个来自生长期行。在采收行数据集中，系统在13.2米飞行中仅用10.5秒即达到94.4%的计数准确率和87.5%的重量估计准确率；对于包含遮挡未熟果实的生长行数据集，我们定性分析了跟踪性能，并指出了未来在强遮挡温室中提升感知能力的研究方向。结果表明，无人机在商业化温室中具备高效机器人估产的潜力。
原文摘要 · Abstract (English)
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.
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