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Predicting Crop Production for Agricultural Consultation Service

  • Lee, Soong-Hee (Department of Information and Communications Engineering, Inje University) ;
  • Bae, Jae-Yong (Department of Information and Communications Engineering, Inje University)
  • Received : 2018.10.03
  • Accepted : 2019.01.08
  • Published : 2019.03.31

Abstract

Smart Farming has been regarded as an important application in information and communications technology (ICT) fields. Selecting crops for cultivation at the pre-production stage is critical for agricultural producers' final profits because over-production and under-production may result in uncountable losses, and it is necessary to predict crop production to prevent these losses. The ITU-T Recommendation for Smart Farming (Y.4450/Y.2238) defines plan/production consultation service at the pre-production stage; this type of service must trace crop production in a predictive way. Several research papers present that machine learning technology can be applied to predict crop production after related data are learned, but these technologies have little to do with standardized ICT services. This paper clarifies the relationship between agricultural consultation services and predicting crop production. A prediction scheme is proposed, and the results confirm the usability and superiority of machine learning for predicting crop production.

Keywords

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Fig. 1. A reference architecture for an agricultural consultation service (ITU-T Y.smpp [2]).

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Fig. 2. Predicting crop production with an agricultural consultation service.

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Fig. 3. Flow chart of the proposed prediction scheme.

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Fig. 4. Predicted results after 1000, 10000, and 100000 iterations of learning with input data from 1997 to 2016.

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Fig. 5. Predicted rice production in Seoul at 2017.

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Fig. 6. Comparison between predicted rice production and actual rice production in 2017 per each district.

Table 1. Input data for machine learning for the proposed scheme (fromKOSIS website)

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Table 2. Accuracy of predictions for rice production in each district

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