Abstract
In the case of autonomous orchard navigation, researchers have developed algorithms that utilize features, such as trunks, canopies, and sky in orchards, but there are still various difficulties in recognizing free space for autonomous navigation in a changing agricultural environment. In this study, we applied the Naive Bayesian classification to detect the boundary between the trunk and the ground and propose an algorithm to determine the center line of free space. The naïve Bayesian classification requires a small number of samples for training and a simple training process. In addition, it was able to effectively classify tree trunk’s points and noise points of the orchard, which are problematic in vision-based processing, and noise caused by small branches, soil, weeds, and tree shadows on the ground. The performance of the proposed algorithm was investigated using 229 sample images obtained from an image acquisition system with a Complementary Metal Oxide Semiconductor (CMOS) Image Sensor (CIS) camera. The center line detected by the unaided-eye manual decision and the results extracted by the proposed algorithm were compared and analyzed for several parameters. In all compared parameters, extracted center line was more stable than the manual center line results.
Highlights
It is said that agriculture, such as rice domestication, began around 10,000 B.C
One point on the center line detected through the proposed algorithm is the target point for the time point for unmanned ground vehicle (UGV) travel
We have developed an algorithm for the autonomous orchard navigation of a UGV and compared the performance with manually unaided eye-decision results
Summary
It is said that agriculture, such as rice domestication, began around 10,000 B.C. After that, civilization started based on agriculture and the agriculture technology has advanced to these days over human history. In the case of apple orchards, it is an outdoor natural environment and the apple trees are planted in straight, parallel lines, and the distance between two row lines is almost the same and the distance between each tree in a row is almost the same It is suitable for location recognition and movement path generation of a mobile robot. Laser sensors were used to locate moving machines These results show that the tree trunk detection algorithm is constrained when trunks are covered with fallen tree branches and leaves [9,13,16]. The orchard weeds cover the trunk-to-ground boundary, and the ground is irregularly composed of soils and weeds, often making the bottom pattern uneven For this reason, it becomes more difficult to determine the travelable route based on tree trunks using machine vision. 2018, 10, 355line of the orchard alley using linear regression analysis, and the alley center of free space center line can be used as a movement path or navigation information for the UGV
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