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An effective data mining techniques based optimal paddy yield cultivation: a rational approach

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Abstract

From ancient times, the economic growth of India is mainly based on agricultural output. Agriculture is demographically the broadest economical sector and acts as an important part in the entire socio-economic fabric of India. Nearly half of the population of our country are connected to agriculture as their industry and make a livelihood out of it. At the same time, the crop productivity depends upon based on several climatic and economical factors namely soil type, weather, irrigation, fertilizer, rainfall, and so on. The present advancements in Information Technology for agriculture field become a hot research topic for predicting the crop yield, which can be resolved by the use of data mining techniques. This paper focuses to design a new predictive approach which offers a high yield of paddy crops by the use of data mining models and Hungarian model in Kuruvai season which generally starts from June to July. The data set used in this research for mining process is real data of Kuruvai season are collected from Tamil Nadu Agricultural University (TNAU) AgriTech Portal, Aduthurai, Thanjavur district and a set of three data mining approaches namely Apriori method, Naive Bayes (NB) and J48 classifier are employed to predict the paddy yield. The outcome of various models has been analyzed, and it is shown that the NB model has offered superior outcome over the compared classifier models.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, (Deemed To Be University), Chennai, TamilNadu, India

    B. Vinoth

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

    N. M. Elango

Authors
  1. B. Vinoth
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  2. N. M. Elango
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Correspondence to B. Vinoth.

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Vinoth, B., Elango, N.M. An effective data mining techniques based optimal paddy yield cultivation: a rational approach. Paddy Water Environ 19, 331–343 (2021). https://doi.org/10.1007/s10333-021-00845-8

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  • DOI: https://doi.org/10.1007/s10333-021-00845-8

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