Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology

Abstract

An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of 0.094 s in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of 0.113 s . The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of 140 × 260 pixels could achieve high-precision vision navigation while the course deviation angle was not more than 7.5 ° . The maximum tractor speed of the optimal template and global template were 51.41 km / h and 27.47 km / h , respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect.

Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain

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Trajectory tracking for agricultural tractor based on improved fuzzy sliding mode control

“…For instance, to reduce the lateral deviations of path tracking for an autonomous guidance tractor in paddy fields, Han et al [ 15 ] developed an improved path tracking controller and built a test platform for a real tractor equipped with an RTK-GPS and IMU system. To meet the real-time vision navigation of the smart tractor in a complex agricultural field environment, Lu et al [ 16 ] proposed an improved anti-noise morphology vision navigation algorithm and a camera and RTK-GPS were adopted on the tractor. However, the positioning accuracy of GPS is vulnerable to obstacles and electromagnetic interference around the farmland, and RTK consumes relatively large amounts of power, which makes it difficult to ensure the continuity of the navigation system.…”