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An Improved DeepLabV3+-Based Method for Crop Row Segmentation and Navigation Line Extraction in Agricultural Fields
Letian Wu1,2, Yongzhi Cui3,4, Huifeng Shi2
1Institute of Agricultural Equipment, Xinjiang Uygur Autonomous Region Academy of Agricultural Sciences, Urumqi 830091, China.
Sensors (Basel, Switzerland)
|May 27, 2026
Summary
This study introduces an improved crop row segmentation and navigation method for autonomous agricultural vehicles. The novel approach enhances accuracy and real-time performance, enabling precise navigation in complex fields.
Area of Science:
- Agricultural Engineering
- Computer Vision
- Robotics
Background:
- Accurate crop row detection is crucial for autonomous agricultural navigation.
- Existing methods struggle with accuracy and real-time performance in complex field conditions.
Purpose of the Study:
- To develop an improved crop row segmentation and navigation method balancing accuracy, robustness, and real-time performance.
- To enhance feature representation and contextual capture for better segmentation.
Main Methods:
- Utilized the DeepLabV3+ framework with MobileNetV2 backbone for computational efficiency.
- Integrated attention mechanisms (split-attention convolution, CBAM) and multi-scale fusion (DenseASPP + SP module).
- Employed DBSCAN clustering and RANSAC fitting for generating high-precision navigation lines from detected crop row anchor points.
Main Results:
- Achieved a mean Intersection over Union (mIoU) of 93.42% and an f1-score of 96.8%, outperforming mainstream models.
- Maintained a lightweight architecture (8.35 M parameters) with real-time processing speed (32 FPS).
- Demonstrated high fitting accuracy for navigation lines, particularly for the middle crop row, with minimal errors.
Conclusions:
- The proposed method offers an efficient visual perception solution for intelligent agricultural operations.
- The enhanced DeepLabV3+ model provides a robust and accurate system for autonomous agricultural navigation.
- This research contributes to advancing precision agriculture through improved machine vision capabilities.
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