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Agriculture
Volume 16
Issue 1
10.3390/agriculture16010100
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Vision-Guided Cleaning System for Seed-Production Wheat Harvesters Using RGB-D Sensing and Object Detection

by
Junjie Xia
1,
Xinping Zhang
1,
Jingke Zhang
1,
Cheng Yang
2,
Guoying Li
2,
Runzhi Yu
1 and
Liqing Zhao
1,*
1
College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
2
Qingdao Plantech Mechanical Technology Co., Ltd., Qingdao 266109, China
*
Author to whom correspondence should be addressed.
Agriculture 2026, 16(1), 100; https://doi.org/10.3390/agriculture16010100
Submission received: 2 December 2025 / Revised: 21 December 2025 / Accepted: 29 December 2025 / Published: 31 December 2025
(This article belongs to the Section Agricultural Technology)
Versions Notes

Abstract

Residues in the grain tank of seed-production wheat harvesters often cause varietal admixture, challenging seed purity maintenance above 99%. To address this, an intelligent cleaning system was developed for automatic residue recognition and removal. The system utilizes an RGB-D camera and an embedded AI unit paired with an improved lightweight object detection model. This model, enhanced for feature extraction and compressed via LAMP, was successfully deployed on a Jetson Nano, achieving 92.5% detection accuracy and 13.37 FPS for real-time 3D localization of impurities. A D–H kinematic model was established for the 4-DOF cleaning manipulator. By integrating the PSO and FWA models, the motion trajectory was optimized for time-optimality, reducing movement time from 9 s to 5.96 s. Furthermore, a gas–solid coupled simulation verified the separation capability of the cyclone-type dust extraction unit, which prevents motor damage and centralizes residue collection. Field tests confirmed the system’s comprehensive functionality, achieving an average cleaning rate of 92.6%. The proposed system successfully enables autonomous residue cleanup, effectively minimizing the risk of variety mixing and significantly improving the harvest purity and operational reliability of seed-production wheat. It presents a novel technological path for efficient seed production under the paradigm of smart agriculture.
Keywords: wheat harvester; seed production; object detection; edge device deployment; harvester cleaning; agricultural robot wheat harvester; seed production; object detection; edge device deployment; harvester cleaning; agricultural robot

Share and Cite

MDPI and ACS Style

Xia, J.; Zhang, X.; Zhang, J.; Yang, C.; Li, G.; Yu, R.; Zhao, L. Vision-Guided Cleaning System for Seed-Production Wheat Harvesters Using RGB-D Sensing and Object Detection. Agriculture 2026, 16, 100. https://doi.org/10.3390/agriculture16010100

AMA Style

Xia J, Zhang X, Zhang J, Yang C, Li G, Yu R, Zhao L. Vision-Guided Cleaning System for Seed-Production Wheat Harvesters Using RGB-D Sensing and Object Detection. Agriculture. 2026; 16(1):100. https://doi.org/10.3390/agriculture16010100

Chicago/Turabian Style

Xia, Junjie, Xinping Zhang, Jingke Zhang, Cheng Yang, Guoying Li, Runzhi Yu, and Liqing Zhao. 2026. "Vision-Guided Cleaning System for Seed-Production Wheat Harvesters Using RGB-D Sensing and Object Detection" Agriculture 16, no. 1: 100. https://doi.org/10.3390/agriculture16010100

APA Style

Xia, J., Zhang, X., Zhang, J., Yang, C., Li, G., Yu, R., & Zhao, L. (2026). Vision-Guided Cleaning System for Seed-Production Wheat Harvesters Using RGB-D Sensing and Object Detection. Agriculture, 16(1), 100. https://doi.org/10.3390/agriculture16010100

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