Extracting the central crop row with CCRDNet for universal in-row navigation in agriculture
Hao Zheng, Qiang Wang

TL;DR
This paper introduces CCRDNet, a lightweight deep learning model that extracts central crop rows for universal in-row navigation in agriculture, achieving high accuracy and real-time performance.
Contribution
The novel CCRDNet model simplifies navigation by directly extracting central crop rows with minimal training data and generalizes to unseen environments.
Findings
CCRDNet achieved 95.57% navigation line extraction accuracy with an average angle error of 1.13°.
The model operates at 86.76 FPS on an RTX 3060 GPU and 48.78 FPS on a Jetson Orin NX.
It enables zero-shot generalization across diverse agricultural environments with only 400 training images.
Abstract
Deep learning has recently shown strong potential in crop row detection for navigation line extraction. However, existing approaches often rely on dataset-specific customization and extensive image preprocessing, limiting their practicality in real-world agricultural scenarios. In contrast, human operators can instinctively navigate machinery by simply following the central crop row. Inspired by this observation, we propose a novel strategy that directly extracts the central crop row as the navigation line. To support this paradigm, we introduce a three-class annotation scheme—background, vegetation, and central crop row—where the vegetation class serves as an auxiliary supervisory signal to provide structural constraints and guide accurate localization. A consistent annotation width of crop row is applied across all samples to enable the model to learn invariant structural features. We…
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Taxonomy
TopicsSmart Agriculture and AI · Plant Surface Properties and Treatments · Agricultural Engineering and Mechanization
