Inter-row navigation line detection for cotton with broken rows

Abstract

Abstract Background The application of autopilot technology is conductive to achieving path planning navigation and liberating labor productivity. In addition, the self-driving vehicles can drive according to the growth state of crops to ensure the accuracy of spraying and pesticide effect. Navigation line detection is the core technology of self-driving technology, which plays a more important role in the development of Chinese intelligent agriculture. The general algorithms for seedling line extraction in the agricultural fields are for large seedling crops. At present, scholars focus more on how to reduce the impact of crop row adhesion on extraction of crop rows. However, for seedling crops, especially double-row sown seedling crops, the navigation lines cannot be extracted very effectively due to the lack of plants or the interference of rut marks caused by wheel pressure on seedlings. To solve these problems, this paper proposed an algorithm that combined edge detection and OTSU to determine the seedling column contours of two narrow rows for cotton crops sown in wide and narrow rows. Furthermore, the least squares were used to fit the navigation line where the gap between two narrow rows of cotton was located, which could be well adapted to missing seedlings and rutted print interference. Results The algorithm was developed using images of cotton at the seedling stage. Apart from that, the accuracy of route detection was tested under different lighting conditions and in maize and soybean at the seedling stage. According to the research results, the accuracy of the line of sight for seedling cotton was 99.2%, with an average processing time of 6.63 ms per frame; the accuracy of the line of sight for seedling corn was 98.1%, with an average processing time of 6.97 ms per frame; the accuracy of the line of sight for seedling soybean was 98.4%, with an average processing time of 6.72 ms per frame. In addition, the standard deviation of lateral deviation is 2 cm, and the standard deviation of heading deviation is 0.57 deg. Conclusion The proposed rows detection algorithm could achieve state-of-the-art performance. Besides, this method could ensure the normal spraying speed by adapting to different shadow interference and the randomness of crop row growth. In terms of the applications, it could be used as a reference for the navigation line fitting of other growing crops in complex environments disturbed by shadow.