Autonomous detection of crop rows based on adaptive multi-ROI in maize fields

Crop rows detection in maize fields remains a challenging problem due to variation in illumination and weeds interference under field conditions. This study proposed an algorithm for detecting crop rows based on adaptive multi-region of interest (multi-ROI). First, the image was segmented into crop and soil and divided into several horizontally labeled strips. Feature points were located in the first image strip and initial ROI was determined. Then, the ROI window was shifted upward. For the next image strip, the operations for the previous strip were repeated until multiple ROIs were obtained. Finally, the least square method was carried out to extract navigation lines and detection lines in multi-ROI. The detection accuracy of the method was 95.3%. The average computation time was 240.8 ms. The results suggest that the proposed method has generally favorable performance and can meet the real-time and accuracy requirements for field navigation.

Automated summary

The study proposed an algorithm for detecting crop rows in maize fields based on adaptive multi-region of interest (multi-ROI), achieving a detection accuracy of 95.3% with an average computation time of 240.8 ms. The results indicate that the method performs well in field navigation and meets real-time and accuracy requirements.