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Design and Test of Air-Assisted Seed-Guiding Device of Precision Hill-Seeding Centralized Seed-Metering Device for Sesame

Author

Listed:
  • Baoshan Wang

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
    Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China)

  • Qingxi Liao

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
    Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China)

  • Lei Wang

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
    Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China)

  • Caixia Shu

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
    Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China)

  • Mei Cao

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
    Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China)

  • Wenbin Du

    (College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
    Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China)

Abstract

Sesame seeds are flat and oval, with poor mobility, easily blocking a seed tube and reducing seeding quality. An air-assisted seed-guiding device was designed for a hill-seeding centralized seed-metering device for sesame. The core of the seed-guiding device is a distribution manifold that could restrict the trajectory of seeds and make seeds move in the same direction as airflow. Six-factor three-level orthogonal tests were carried out using CFD–DEM coupling simulation to study the influence of the structure and operation parameters of the seed-guiding device on airflow field, seed transport, and seeding performance. The simulation results derived optimal parameters: the depth of the circular section of the seed slide was 2.62 mm, the length of the expansion and contraction section was 188 mm and the length of the contraction section was 20 mm, the seed tube diameter was 19 mm, the airflow velocity was 6.3 m/s, and the rotation speed of the roller was 25 r/min. Under the optimal parameters, the positive pressure required for the seed-guiding device was 256.77 Pa, the time of seeds passing through the seed-guiding device was 0.77 ± 0.02 s, and the velocity of seeds when they came out of the seed tubes was 2.24 ± 0.30 m/s. The qualified rate was 88.33% (2 ± 1 seeds/hill), and the miss-seeding rate was 5.00% (0 seeds/hill). Bench test showed that the qualified rate was 86.80%, and the miss-seeding rate was 6.00%. The seeding performance of the bench test was consistent with the simulation results. Field tests showed that the average number of seedlings per hill was 1.32. The seed-guiding device could meet the requirements of precision hill-seeding for sesame. This study provides a reference for design of a seed-guiding device of a centralized seed-metering device for sesame.

Suggested Citation

  • Baoshan Wang & Qingxi Liao & Lei Wang & Caixia Shu & Mei Cao & Wenbin Du, 2023. "Design and Test of Air-Assisted Seed-Guiding Device of Precision Hill-Seeding Centralized Seed-Metering Device for Sesame," Agriculture, MDPI, vol. 13(2), pages 1-21, February.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:393-:d:1060961
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    References listed on IDEAS

    as
    1. Zhijian Wang & Qi Zhou & Senouwa Segla Koffi Dossou & Rong Zhou & Yingzhong Zhao & Wangyi Zhou & Yanxin Zhang & Donghua Li & Jun You & Linhai Wang, 2022. "Genome-Wide Association Study Uncovers Loci and Candidate Genes Underlying Phytosterol Variation in Sesame ( Sesamum indicum L.)," Agriculture, MDPI, vol. 12(3), pages 1-13, March.
    2. Mahdieh Parsaeian & Mohammad Rahimi & Abbas Rohani & Shaneka S. Lawson, 2022. "Towards the Modeling and Prediction of the Yield of Oilseed Crops: A Multi-Machine Learning Approach," Agriculture, MDPI, vol. 12(10), pages 1-23, October.
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