International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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A Prediction of Nutrition Water for Strawberry Production using Linear Regression

  • Venkatesan, Saravanakumar (Department of Information and Communication Engineering, Sunchon National University) ;
  • Sathishkumar, VE (Department of Information and Communication Engineering, Sunchon National University) ;
  • Park, Jangwoo (Department of Information and Communication Engineering, Sunchon National University) ;
  • Shin, Changsun (Department of Information and Communication Engineering, Sunchon National University) ;
  • Cho, Yongyun (Department of Information and Communication Engineering, Sunchon National University)
  • Received : 2020.02.02
  • Accepted : 2020.02.12
  • Published : 2020.03.31
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Abstract

It is very important to use appropriate nutrition water for crop growth in hydroponic farming facilities. However, in many cases, the supply of nutrition water is not designed with a precise plan, but is performed in a conventional manner. We proposes a forecasting technique for nutrition water requirements based on a data analysis for optimal strawberry production. To do this, the proposed forecasting technique uses linear regression for correlating strawberry production, soil condition, and environmental parameters with nutrition water demand for the actual two-stage strawberry production soil. Also, it includes predicting the optimal amount of nutrition water requires according to the heterogeneous cultivation environment and variety by comparing the amount of nutrition water needed for the growth and production of different kinds of strawberries. We suggested study uses two types of section beds that are compared to find out the best section bed production of strawberry growth. The dataset includes 233 samples collected from a real strawberry greenhouse, and the four predicted variables consist of the total amounts of nutrition water, average temperature, humidity, and CO2 in the greenhouse.

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References

  1. Akin, M., and S. P. Eyduran. "Forecasting harvest area and production of strawberry using time series analyses." Gaziosmanpasa Universitesi Ziraat Fakultesi Dergisi, Vol. 34, No. 3, pp. 18-26, October 2017. DOI: https://doi.org/10.13002/jafag4298
  2. Wrzesien, Mariusz, Waldemar Treder, Krzysztof Klamkowski, and Witold R. Rudnicki. "Prediction of the apple scab using machine learning and simple weather stations." Computers and Electronics in Agriculture, 161, pp. 252- 259, June 2019. DOI: https://doi.org/10.1016/j.compag.2018.09.026
  3. Shuaishuai, Li, Li Li, Meng Fanjia, Wang Haihua, Su Zhanzhan, and N. A. Sigrimis. "Prediction Model of Transpiration Rate of Strawberry in Closed Cultivation Based on DBN-LSSVM Algorithm." IFAC-PapersOnLine, Vol. 51, No. 17, pp. 460-465, January 2018. DOI: https://doi.org/10.1016/j.ifacol.2018.08.171
  4. Liu, Changhong, Wei Liu, Xuzhong Lu, Fei Ma, Wei Chen, Jianbo Yang, and Lei Zheng. "Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit." PloS one, Vol. 9, No. 2, pp. 1-8 February 2014. DOI: https://doi.org/10.1371/journal.pone.0087818
  5. ElMasry, Gamal, Ning Wang, Adel ElSayed, and Michael Ngadi. "Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry." Journal of Food Engineering, Vol. 81, No. 1 pp. 98-107, July 2007. DOI: https://doi.org/10.1016/j.jfoodeng.2006.10.016
  6. Guo-Feng, P. A. N. "Research on spectral detection of nitrate in water quality based on K-S algorithm." Chinese Journal of Spectroscopy Laboratory, Vol. 5 , 2011.
  7. Jouquand, Céline, Craig Chandler, Anne Plotto, and Kevin Goodner. "A sensory and chemical analysis of fresh strawberries over harvest dates and seasons reveals factors that affect eating quality." Journal of the American Society for Horticultural Science, Vol. 133, No. 6, pp. 859-867, November 2008. DOI: https://doi.org/10.21273/JASHS.133.6.859
  8. MacKenzie, S. J., and C. K. Chandler. "The late season decline in strawberry fruit soluble solid content observed in Florida is caused by rising temperatures." Acta horticulturae, Vol. 842, pp. 843-846, August 2009. DOI: https://doi.org/10.17660/ActaHortic.2009.842.186
  9. Vicente, Esteban, Pablo Varela, Luis de Saldamando, and Gaston Ares. "Evaluation of the sensory characteristics of strawberry cultivars throughout the harvest season using projective mapping." Journal of the Science of Food and Agriculture, Vol. 94, No. 3, pp. 591-599, February 2014. DOI: https://doi.org/10.1002/jsfa.6307
  10. Amid, Sama, and Tarahom Mesri Gundoshmian. "Prediction of output energies for broiler production using linear regression, ANN (MLP, RBF), and ANFIS models." Environmental Progress & Sustainable Energy, Vo. 36, No. 2, pp. 577-585, March 2017. DOI: https://doi.org/10.1002/ep.12448
  11. Jangmook Kang, and Sangwon Lee, "Algorithm Design to Judge Fake News based on Bigdata and Artificial Intelligence", IJIBC, Vol. 11, No. 2, pp. 50-58, June 2019. DOI: https://doi.org/10.7236/IJIBC.2019.11.2.50
  12. ByungJoo Kim, "Ensemble Methods Applied to Classification Problem", IJIBC, Vol. 11, No. 1, pp. 47-53, March 2019. DOI: https://doi.org/10.7236/IJIBC.2019.11.1.47