Application of Decision Tree (M5tree) algorithm for multicrop yield prediction of the semi-arid region of Maharashtra, India

Articles in Press

Document Type : Regular Article

Authors

1 Civil Engineering Department, National Institute of Technology, Surat (Gujarat), India

2 Civil Engineering Deapartment, National Institute of Technology, Surat (Gujarat), India

10.22115/scce.2024.383387.1601

Abstract

ABSTRACT
India, with a population of 1.40 billion, is the most populous country in the world, necessitating increased food production. For making decisions about issues relating to food security, reliable crop yield estimation is essential. Knowing the expected yield of one's standing crops is crucial to farmers and can be a complicated task in and of itself. Modern artificial intelligence algorithms have shown to be highly useful tools for accurately predicting agricultural production. The primary focus of this study is on predicting the yield of five important crops grown in the Nashik region. Crop yield is significantly influenced by climatic variables such as rainfall, minimum and maximum temperatures, relative humidity, and evaporation. In this regard, we used the Decision tree (M5tree) method to predict the yield of five important crops farmed in the Nashik region of Maharashtra state in India: rice, jowar, maize, groundnut, and sugarcane. With acceptable accuracy, the developed models have functioned well. The association between climatic variables and the agricultural production of the crops under study was disclosed by the decision trees, and the rule accuracy validated this relationship.

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Main Subjects

Journal of Soft Computing in Civil Engineering

Articles in Press, Accepted Manuscript
Available Online from 19 May 2024

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History

  • Receive Date: 27 January 2023
  • Revise Date: 25 December 2023
  • Accept Date: 24 February 2024

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