A Unified Framework for Enhanced 3D Spatial Localization of Weeds via Keypoint Detection and Depth Estimation

Shuxin Xie, Tianrui Quan, Junjie Luo, Xuesong Ren and Yubin Miao ()
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Shuxin Xie: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Tianrui Quan: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Junjie Luo: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Xuesong Ren: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Yubin Miao: School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Agriculture, 2025, vol. 15, issue 17, 1-26

Abstract: In this study, a lightweight deep neural network framework WeedLoc3D based on multi-task learning is proposed to meet the demand of accurate three-dimensional positioning of weed targets in automatic laser weeding. Based on a single RGB image, it both locates the 2D keypoints (growth points) of weeds and estimates the depth with high accuracy. This is a breakthrough from the traditional thinking. To improve the model performance, we introduce several innovative structural modules, including Gated Feature Fusion (GFF) for adaptive feature integration, Hybrid Domain Block (HDB) for dealing with high-frequency details, and Cross-Branch Attention (CBA) for promoting synergy among tasks. Experimental validation on field data sets confirms the effectiveness of our method. It significantly reduces the positioning error of 3D keypoints and achieves stable performance in diverse detection and estimation tasks. The demonstrated high accuracy and robustness highlight its potential for practical application.

Keywords: weed detection; multi-task learning; keypoint localization; depth estimation (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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