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The Potential Future Of Agriculture For Small Farms: Supervised Machine-Learning Smart Irrigation Concept For Vegetables

Author

Listed:
  • Arnesh Telukdarie

    (Johannesburg Business School)

  • Noluthando Gamede

    (Johannesburg Business School)

  • Inderasan Munien

    (Johannesburg Business School)

  • Andre Vermeulen

    (Johannesburg Business School)

  • Uche Onkonkwo

    (Johannesburg Business School)

Abstract

Sustainability is a crucial concept in agriculture and agricultural production. Since there is an intense competitiveness and risk in the sector, technical advancements are essential for improved development and sustainability. Small farms require cost effective solutions to match the standards of bigger producers, which essentially means best yields on crops, both in terms of quantity and quality. It is crucial to consider water crisis, climate change, and quality farm care when designing new solutions for agriculture. This study proposes the automation of farm irrigation systems based on a supervised machine learning model (SVM, Logistic Regression) that is cost-effective and precise response to farming demands. In order to show the practicality of this proposed new technology, an application was developed to represent the model results. The application created was for the user to be able to use the model to predict and control the valves in an irrigation system. This work is a potential solution for small farms in a country like South Africa, where sustainable farming is taking rise.

Suggested Citation

  • Arnesh Telukdarie & Noluthando Gamede & Inderasan Munien & Andre Vermeulen & Uche Onkonkwo, 2023. "The Potential Future Of Agriculture For Small Farms: Supervised Machine-Learning Smart Irrigation Concept For Vegetables," Big Data In Agriculture (BDA), Zibeline International Publishing, vol. 5(2), pages 57-63, July.
    • Handle: RePEc:zib:zbnbda:v:5:y:2023:i:2:p:57-63
    DOI: 10.26480/bda.02.2023.57.63
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    References listed on IDEAS

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    3. Zema, Demetrio Antonio & Nicotra, Angelo & Mateos, Luciano & Zimbone, Santo Marcello, 2018. "Improvement of the irrigation performance in Water Users Associations integrating data envelopment analysis and multi-regression models," Agricultural Water Management, Elsevier, vol. 205(C), pages 38-49.
    4. Pereira, L.S. & Paredes, P. & Jovanovic, N., 2020. "Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach," Agricultural Water Management, Elsevier, vol. 241(C).
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