Crop-Free-Ridge Navigation Line Recognition Based on the Lightweight Structure Improvement of YOLOv8

Runyi Lv, Jianping Hu (), Tengfei Zhang, Xinxin Chen and Wei Liu
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Runyi Lv: Jiangsu Provincial Key Laboratory of Hi-Tech Research for Intelligent Agricultural Equipment, Jiangsu University, Zhenjiang 212013, China
Jianping Hu: Jiangsu Provincial Key Laboratory of Hi-Tech Research for Intelligent Agricultural Equipment, Jiangsu University, Zhenjiang 212013, China
Tengfei Zhang: Jiangsu Provincial Key Laboratory of Hi-Tech Research for Intelligent Agricultural Equipment, Jiangsu University, Zhenjiang 212013, China
Xinxin Chen: Jiangsu Provincial Key Laboratory of Hi-Tech Research for Intelligent Agricultural Equipment, Jiangsu University, Zhenjiang 212013, China
Wei Liu: Jiangsu Provincial Key Laboratory of Hi-Tech Research for Intelligent Agricultural Equipment, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 9, 1-16

Abstract: This study is situated against the background of shortages in the agricultural labor force and shortages of cultivated land. In order to improve the intelligence level and operational efficiency of agricultural machinery and solve the problems of difficulties in recognizing navigation lines and a lack of real-time performance of transplanters in the crop-free ridge environment, we propose a crop-free-ridge navigation line recognition method based on an improved YOLOv8 segmentation algorithm. First, this method reduces the parameters and computational complexity of the model by replacing the YOLOv8 backbone network with MobileNetV4 and the feature extraction module C2f with ShuffleNetV2, thereby improving the real-time segmentation of crop-free ridges. Second, we use the least-squares method to fit the obtained point set to accurately obtain navigation lines. Finally, the method is applied to testing and analyzing the field experimental ridges. The results showed that the average precision of the improved neural network model using this method was 90.4%, with a Params of 1.8 M, a FLOPs of 8.8 G, and an FPS of 49.5. The results indicate that the model maintains high accuracy while significantly outperforming Mask-RCNN, YOLACT++, YOLOv8, and YOLO11 in terms of computational speed. The detection frame rate increased significantly, improving the real-time performance of detection. This method uses the least-squares method to fit the 55% ridge contour feature points under the picture, and the fitting navigation line shows no large deviation compared with the image ridge centerline; the result is better than that of the RANSAC fitting method. The research results indicate that this method significantly reduces the size of the model parameters and improves the recognition speed, providing a more efficient solution for the autonomous navigation of intelligent carrier aircraft.

Keywords: navigation line; autonomous navigation; deep learning; crop-free ridge; least-square method (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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