自主农业拖拉机：用于精准稻田种植的杂草检测与激光雷达导航集成系统

中文摘要 AI 辅助

稻田种植中针对特定区域的杂草管理相比传统的大面积喷洒可大幅减少除草剂使用，但现场部署受三个持续存在的挑战限制：在GNSS性能下降的冠层下实现稳健的作物行导航、实时视觉区分水稻与形态多样的杂草，以及将水稻误分类为杂草的成本不对称且不可逆转。本文提出AgriNav，一种围绕四个ROS耦合模块构建的集成自主拖拉机系统：WeedDet的自定义PyTorch重实现用于水稻检测、带有非对称类别权重的并行轻量级168万参数CNN-FPN变体、通过硬编码置信度门否决机制保护水稻类别的反逻辑判别模块，以及融合GNSS、IMU和轮式里程计并具备三级中断桥接的6状态恒速转弯率扩展卡尔曼滤波器。我们的主要系统级贡献是一种四机制激光雷达-相机融合桥，其利用导航激光雷达实现感兴趣区域约束、世界坐标投影、地面平面滤波和双向置信度融合，且无需额外硬件成本。仿真实验表明，在20秒的GNSS中断期间可实现连续位置跟踪，整个操作过程中作物行检测置信度高于0.9，在稻田、航拍及后洪水图像中水稻检测置信度为0.32至0.95；激光雷达感兴趣区域约束使检测推理区域估计减少30%至50%。

英文摘要

Site-specific weed management in paddy farming offers substantial reductions in herbicide use over conventional broadcast spraying, but field deployment has been limited by three persistent challenges: robust crop-row navigation under canopy where GNSS degrades, real-time visual discrimination between rice and morphologically diverse weeds, and the asymmetric cost of misclassifying rice as weed, which is irreversible. This paper presents AgriNav, an integrated autonomous tractor system built around four ROS-coupled modules: a custom PyTorch reimplementation of WeedDet for rice detection, a parallel lightweight 1.68M-parameter CNN-FPN variant with asymmetric class weighting, an inverted-logic discrimination module that protects the rice class through a hardcoded confidence-gate veto, and a 6-state constant-velocity-turn-rate Extended Kalman Filter fusing GNSS, IMU, and wheel odometry with three-level outage bridging. Our primary system-level contribution is a four-mechanism LiDAR-camera fusion bridge that uses the navigation LiDAR for region-of-interest constraint, world-coordinate projection, ground-plane filtering, and bidirectional confidence fusion at zero additional hardware cost. Simulation experiments demonstrate continuous position tracking through a 20-second GNSS outage, crop row detection confidence above 0.9 throughout operation, and rice-detection confidences from 0.32 to 0.95 across paddy, aerial, and post-flood imagery. The LiDAR ROI constraint reduces detection inference region by an estimated 30 to 50 percent.

发表机构

• Florida Gulf Coast University（佛罗里达湾岸大学）

机构由 AI 辅助整理，请以论文原文为准。