Classification of Sugarcane Billet Using Computer Vision

Authors

  • Devayani Suryavanshi M.Tech. Student, Department of Mechanical Engineering, Padmabhooshan Vasantraodada Patil Institute of Technology, Budhgaon, India
  • D. P. Patil Professor, Department of Mechanical Engineering, Padmabhooshan Vasantraodada Patil Institute of Technology, Budhgaon, India
  • S. S. Gunjate Professor, Department of Mechanical Engineering, Padmabhooshan Vasantraodada Patil Institute of Technology, Budhgaon, India

Keywords:

Arduino Uno, Billet, HSV model, Image processing, RGB model

Abstract

One of the widely taken crop is sugarcane. Sugarcane is very important raw material for the production of sugar, ethanol, and bagasse. In most of countries harvesting machines are used. Recently plantation of sugarcane is done by using billets. But it has disadvantage also because when forming billets by harvesting machine it gets damaged and causes billet quality degradation and spreading of diseases. Here we developed a prototype model to classify the good and damaged billets separately. This model runs on Python program which segregate healthy (good) and damaged billets with the help of image processing technique after taking pictures of these billets using USB camera. So, the good billets goes for next planting method and remaining billets i.e. damaged billets used for sugar recovery and other by-products.

 

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Published

31-08-2020

How to Cite

[1]
D. Suryavanshi, D. P. Patil, and S. S. Gunjate, “Classification of Sugarcane Billet Using Computer Vision”, IJRESM, vol. 3, no. 8, pp. 497–501, Aug. 2020.

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Articles