Crop row centerline extraction method based on regional feature point clustering

To address the issues of low accuracy and high processing time in traditional machine vision methods for crop row detection due to varying crop types, growth backgrounds, and changes in the number of crop rows, this study proposes a crop row centerline extraction method based on regional feature point clustering. First, the 2G-R-B feature factor and an optimal adaptive threshold segmentation method are used to segment crops and background. Then, a morphological operation with closing followed by opening is applied to filter out weeds and crop edges, reducing interference. Next, Harris corner detection technology is used to extract crop feature points, and these feature points are clustered using the DBSCAN algorithm, marking each crop row with a different color. Subsequently, the image is horizontally divided into strips, and the midpoint of each cluster of feature points in each strip is extracted. Finally, the least squares method is used to fit the midpoints of each crop row to obtain the centerline of the crop row. Experimental results show that this method demonstrates high accuracy in extracting crop row centerlines for four types of crops: sweet potato, mint, corn, and peanut. The average row recognition rate reached 98.33%, and the average error angle was 0.95°. Additionally, the average processing time per image was 136.14 ms, saving an average of 69.22 ms compared to the traditional Hough transform and 87.33 ms compared to the skeleton extraction method. In summary, this method provides a more robust solution for crop rows affected by various factors in field environments.