LAUSR

Paddy field management is complicated and labor intensive. Correct row detection is important to automatically track rice rows. In this study, a novel method was proposed for accurate rice row recognition in paddy field transplanted by machine before the disappearance of row information. Firstly, Bayesian decision theory based on the minimum error was used to classify the period of collected images into three periods (T1: 0-7 d; T2: 7-28 d; T3: 28-45 d), and resulting in the correct recognition rate was 97.03%. Moreover, secondary clustering of feature points was proposed, which can solve some problems such as row breaking and tilting. Then, the robust regression least squares method (RRLSM) for linear fitting was proposed to fit rice rows to effectively eliminate interference by outliers. Finally, a credibility analysis of connected region markers was proposed to evaluate the accuracy of fitting lines. When the threshold of credibility was set at 40%, the correct recognition rate of fitting lines was 96.32%. The result showed that the method can effectively solve the problems caused by the presence of duckweed, high-density inter-row weeds, broken rows, tilting (±60°), wind and overlap. Keywords: rice rows detection, Bayesian decision theory, clustering, RRLSM, credibility analysis, automatic tracking DOI: 10.25165/j.ijabe.20211401.5910 Citation: He J, Zang Y, Luo X W, Zhao R M, He J, Jiao J K. Visual detection of rice rows based on Bayesian decision theory and robust regression least squares method. Int J Agric & Biol Eng, 2021; 14(1): 199–206.