Maize is one of the major food crops in China. Traditionally, field operations are done by\nmanual labor, where the farmers are threatened by the harsh environment and pesticides. On the\nother hand, it is difficult for large machinery to maneuver in the field due to limited space, particularly\nin the middle and late growth stage of maize. Unmanned, compact agricultural machines, therefore,\nare ideal for such field work. This paper describes a method of monocular visual recognition to\nnavigate small vehicles between narrow crop rows. Edge detection and noise elimination were used\nfor image segmentation to extract the stalks in the image. The stalk coordinates define passable\nboundaries, and a simplified radial basis function (RBF)-based algorithm was adapted for path\nplanning to improve the fault tolerance of stalk coordinate extraction. The average image processing\ntime, including network latency, is 220 ms. The average time consumption for path planning is 30 ms.\nThe fast processing ensures a top speed of 2 m/s for our prototype vehicle. When operating at the\nnormal speed (0.7 m/s), the rate of collision with stalks is under 6.4%. Additional simulations and\nfield tests further proved the feasibility and fault tolerance of our method.
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