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Development of Seeding Rate Monitoring System Applicable to a Mechanical Pot-Seeding Machine

Author

Listed:
  • Seung-Jun Kim

    (Department of Biosystems Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea
    Interdisciplinary Program in Smart Agriculture, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea
    These authors contributed equally to this work.)

  • Hyeon-Seung Lee

    (Forest Technology and Management Research Center, National Institute of Forest Science, Pocheon 11186, Republic of Korea
    These authors contributed equally to this work.)

  • Seok-Joon Hwang

    (Department of Biosystems Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea
    Interdisciplinary Program in Smart Agriculture, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea)

  • Jeong-Hun Kim

    (Department of Biosystems Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea
    Interdisciplinary Program in Smart Agriculture, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea)

  • Moon-Kyeong Jang

    (Department of Biosystems Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea
    Interdisciplinary Program in Smart Agriculture, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea)

  • Ju-Seok Nam

    (Department of Biosystems Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea
    Interdisciplinary Program in Smart Agriculture, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon 24341, Republic of Korea)

Abstract

In this study, we developed a monitoring system to accurately track the seeding rate and to identify the locations where the mechanical pot-seeding machine failed to sow seeds correctly. The monitoring system employs diverse image processing techniques, including the Hough transform, hue–saturation–value color space conversion, image morphology techniques, and Gaussian blur, to accurately pinpoint the seeding rate and the locations where seeds are missing. To determine the optimal operating conditions for the seeding rate monitoring system, a factorial experiment was conducted by varying the brightness and saturation values of the image data. When the derived optimal operating conditions were applied, the system consistently achieved a 100% seed recognition rate across various seeding conditions. The monitoring system developed in this study has the potential to significantly reduce the labor required for supplementary planting by enabling the real-time identification of locations where seeds were not sown during pot-seeding operations.

Suggested Citation

  • Seung-Jun Kim & Hyeon-Seung Lee & Seok-Joon Hwang & Jeong-Hun Kim & Moon-Kyeong Jang & Ju-Seok Nam, 2023. "Development of Seeding Rate Monitoring System Applicable to a Mechanical Pot-Seeding Machine," Agriculture, MDPI, vol. 13(10), pages 1-18, October.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:10:p:2000-:d:1260114
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