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Current IssueEditorial BoardOnline First ArticlesArchive

Research Article

volume 40 issue 4 (december 2020) : 376-381,   Doi: 10.18805/ag.D-173

Potato Classification by using Ultrasonic Sensor with LabVIEW

A
Abdullah Beyaz
D
Dilara Gerdan
1Ankara University, Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering, 06110, Diskapi, Ankara, Turkey.
Cite article:- Beyaz Abdullah, Gerdan Dilara (2020). Potato Classification by using Ultrasonic Sensor with LabVIEW. Agricultural Science Digest. 40(4): 376-381. doi: 10.18805/ag.D-173.

ABSTRACT

Background: Agricultural product classification is an essential and widely working issue in the agriculture industry. There is a lot of work to be done in this area and the agriculture industry is looking for new applications. The reason is that the software and hardware Technologies are always developing, and the latest technologies giving better results parallel to these developments. Also, the costs of the new technologies are decreasing with the help of low-cost sensor and device technologies. New technique and technology usage also directly related to classification efficiency. The primary data on agriculture are agricultural products, so these technologies and techniques use for agricultural production. 
Methods: The primary data on agriculture are agricultural products, because of this reason, this research aimed to make ultrasonic sensor measurements with the help of a software which is developed in LabVIEW platform for potato classifications and showing the efficiency of the method for the potato classification, which has different sizes.
Result: According to the regression coefficient results, the regression between caliper length measurement (CL) and static ultrasonic sensor length measurement (SUL) found as 95.5%, the regression between caliper length measurement (CL) and dynamic ultrasonic sensor length measurement (DUL) found as 86.9%, the regression between static ultrasonic sensor length measurement (SUL) and dynamic ultrasonic sensor length measurement (DUL) found as 87.9% for potato classification.

REFERENCES

  1. Andújar, D., Weis, M., Gerhards, R. (2012). An Ultrasonic System for Weed Detection in Cereal Crops. Sensors. 12(12): 17343-17357; doi: 10.3390/s121217343.
  2. Beyaz, A. (2008). Determination of Mechanical Damage of Apples by Using Images Analyze Technique. Ankara University, Graduate School of Natural and Applied Sciences, Department of Agricultural Machinery Master’s Thesis.
  3. Fisher, D. K., Sui, R. (2013). An inexpensive open-source ultrasonic sensing system for monitoring liquid levels. Agric Eng Int: CIGR Journal. 15(4): 328-334.
  4. Hammad, A., Hafez, A., Elewa, M.T. (2007). A LabVIEW Based Experimental Platform for Ultrasonic Range Measurements. DSP Journal. 6 (2): 1-8.
  5. Huang, W., Chen, L., Ge, J., Zhang, C. (2012). A Low-Cost Harvest Area Measurement System Based on Ultrasonic and Hall-Effect Sensors. Sensor Letters, 10(1-2): pp. 317-323(7). 
  6. Lijun, Q., Xia, L., Ronghua, J., Jun, W., Hui, L., Pei, W. (2013). 3-D Reconstruction and Measurement of Greenhouse Strawberry Canopy Based on Ultrasonic Sensors. Transactions of the Chinese Society for Agricultural Machinery. Web site:http://en.cnki.com.cn/article_en/cjfdtotalnyjx 201309035. htm, Access date: 15.12.2017.
  7. Moeckel, T., Safari, H., Reddersen, B., Fricke, T., Wachendorf, M. (2017). Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure. Remote Sens. 9(1): 98; doi: 10.3390/rs9010098. 
  8. Mohammed, T. (2009). Using Ultrasonic and Infrared Sensors for Distance Measurement. World Academy of Science, Engineering and Technology. 51: 293-298.
  9. Naik, A. and Panse, M.S. (2012). Modeling of Ultrasonic Sensor for Range Measurement for Visually Impaired. International Journal of Emerging Technology and Advanced Engineering. ISSN 2250-2459, 2(4): 672-675.
  10. Nath, D., Shil, S. (2019). Technological need of True Potato Seed (TPS) growers of Tripura. Agricultural Science Digest. 2018 (38): 304-306 
  11. Reddy. R, Soibam, H., Ayam, V.S., Panja, P. and Mitra, S. (2018). Morphological characterization of sweet potato cultivars during growth, development and harvesting. Indian Journal of Agricultural Research. 2018(52): 46-50.
  12. Sabanci, K, Aydin, C, Ünlerºen, M. (2012). Determination of classification parameters of potatoes with the help of image processing and artificial neural network. Journal of the Institute of Science and Technology, 2 Ek:A (2 Sp:A), 59-62. Retrieved from http://dergipark.org.tr/jist/issue/7930/104312.
  13. Si, Y., Sankaran, S., Knowles, N.R. and Pavek, M.J. (2018). Image-based automated potato tuber shape evaluation. Journal of Food Measurement and Characterization. 12(2): 702-709.
  14. Stajnko, D., Berk, P., Lešnik, M., Jejèiè, V., Lakota, M., Štrancar, A., Hoèevar, M., Rakun, J. (2012). Programmable Ultrasonic Sensing System for Targeted Spraying in Orchards. Sensors, 12(11): 15500-15519; doi: 10.3390/s121115500. 
  15. TSI (2019). TS 1222/December 2008 Potato Standard. Web page:http://www.resmigazete.gov.tr/eskiler/2009/10/200910107-1.doc, Accessed on: 25.06.2019. 
  16. Vidya Shree, G. and Basappa, S. (2017). Non-contact Water Level Monitoring using Labview with Arduino and Ultrasonic Sensor. Proc. of Int. Conf. on Current Trends in Eng., Science and Technology, ICCTEST. 
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    Research Article

    volume 40 issue 4 (december 2020) : 376-381,   Doi: 10.18805/ag.D-173

    Potato Classification by using Ultrasonic Sensor with LabVIEW

    A
    Abdullah Beyaz
    D
    Dilara Gerdan
    1Ankara University, Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering, 06110, Diskapi, Ankara, Turkey.
    Cite article:- Beyaz Abdullah, Gerdan Dilara (2020). Potato Classification by using Ultrasonic Sensor with LabVIEW. Agricultural Science Digest. 40(4): 376-381. doi: 10.18805/ag.D-173.

    ABSTRACT

    Background: Agricultural product classification is an essential and widely working issue in the agriculture industry. There is a lot of work to be done in this area and the agriculture industry is looking for new applications. The reason is that the software and hardware Technologies are always developing, and the latest technologies giving better results parallel to these developments. Also, the costs of the new technologies are decreasing with the help of low-cost sensor and device technologies. New technique and technology usage also directly related to classification efficiency. The primary data on agriculture are agricultural products, so these technologies and techniques use for agricultural production. 
    Methods: The primary data on agriculture are agricultural products, because of this reason, this research aimed to make ultrasonic sensor measurements with the help of a software which is developed in LabVIEW platform for potato classifications and showing the efficiency of the method for the potato classification, which has different sizes.
    Result: According to the regression coefficient results, the regression between caliper length measurement (CL) and static ultrasonic sensor length measurement (SUL) found as 95.5%, the regression between caliper length measurement (CL) and dynamic ultrasonic sensor length measurement (DUL) found as 86.9%, the regression between static ultrasonic sensor length measurement (SUL) and dynamic ultrasonic sensor length measurement (DUL) found as 87.9% for potato classification.

    REFERENCES

    1. Andújar, D., Weis, M., Gerhards, R. (2012). An Ultrasonic System for Weed Detection in Cereal Crops. Sensors. 12(12): 17343-17357; doi: 10.3390/s121217343.
    2. Beyaz, A. (2008). Determination of Mechanical Damage of Apples by Using Images Analyze Technique. Ankara University, Graduate School of Natural and Applied Sciences, Department of Agricultural Machinery Master’s Thesis.
    3. Fisher, D. K., Sui, R. (2013). An inexpensive open-source ultrasonic sensing system for monitoring liquid levels. Agric Eng Int: CIGR Journal. 15(4): 328-334.
    4. Hammad, A., Hafez, A., Elewa, M.T. (2007). A LabVIEW Based Experimental Platform for Ultrasonic Range Measurements. DSP Journal. 6 (2): 1-8.
    5. Huang, W., Chen, L., Ge, J., Zhang, C. (2012). A Low-Cost Harvest Area Measurement System Based on Ultrasonic and Hall-Effect Sensors. Sensor Letters, 10(1-2): pp. 317-323(7). 
    6. Lijun, Q., Xia, L., Ronghua, J., Jun, W., Hui, L., Pei, W. (2013). 3-D Reconstruction and Measurement of Greenhouse Strawberry Canopy Based on Ultrasonic Sensors. Transactions of the Chinese Society for Agricultural Machinery. Web site:http://en.cnki.com.cn/article_en/cjfdtotalnyjx 201309035. htm, Access date: 15.12.2017.
    7. Moeckel, T., Safari, H., Reddersen, B., Fricke, T., Wachendorf, M. (2017). Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure. Remote Sens. 9(1): 98; doi: 10.3390/rs9010098. 
    8. Mohammed, T. (2009). Using Ultrasonic and Infrared Sensors for Distance Measurement. World Academy of Science, Engineering and Technology. 51: 293-298.
    9. Naik, A. and Panse, M.S. (2012). Modeling of Ultrasonic Sensor for Range Measurement for Visually Impaired. International Journal of Emerging Technology and Advanced Engineering. ISSN 2250-2459, 2(4): 672-675.
    10. Nath, D., Shil, S. (2019). Technological need of True Potato Seed (TPS) growers of Tripura. Agricultural Science Digest. 2018 (38): 304-306 
    11. Reddy. R, Soibam, H., Ayam, V.S., Panja, P. and Mitra, S. (2018). Morphological characterization of sweet potato cultivars during growth, development and harvesting. Indian Journal of Agricultural Research. 2018(52): 46-50.
    12. Sabanci, K, Aydin, C, Ünlerºen, M. (2012). Determination of classification parameters of potatoes with the help of image processing and artificial neural network. Journal of the Institute of Science and Technology, 2 Ek:A (2 Sp:A), 59-62. Retrieved from http://dergipark.org.tr/jist/issue/7930/104312.
    13. Si, Y., Sankaran, S., Knowles, N.R. and Pavek, M.J. (2018). Image-based automated potato tuber shape evaluation. Journal of Food Measurement and Characterization. 12(2): 702-709.
    14. Stajnko, D., Berk, P., Lešnik, M., Jejèiè, V., Lakota, M., Štrancar, A., Hoèevar, M., Rakun, J. (2012). Programmable Ultrasonic Sensing System for Targeted Spraying in Orchards. Sensors, 12(11): 15500-15519; doi: 10.3390/s121115500. 
    15. TSI (2019). TS 1222/December 2008 Potato Standard. Web page:http://www.resmigazete.gov.tr/eskiler/2009/10/200910107-1.doc, Accessed on: 25.06.2019. 
    16. Vidya Shree, G. and Basappa, S. (2017). Non-contact Water Level Monitoring using Labview with Arduino and Ultrasonic Sensor. Proc. of Int. Conf. on Current Trends in Eng., Science and Technology, ICCTEST. 
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