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Comparative analysis of machine learning techniques for predicting production capability of crop yield

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  • Kalpana Jain

    (College of Technology and Engineering)

  • Naveen Choudhary

    (College of Technology and Engineering)

Abstract

Recently, the applicability of information technology for quality productivity in agriculture has been increasing progressively. As a result of these, there has been a significant increase in the data obtained from agricultural production. The data obtained from experimental research helps in the decision-making process through machine learning and artificial intelligence. Applications of data mining steps make raw agricultural data more meaningful. Data mining analysis used in many fields brings a variety of benefits to its use in agricultural production. This paper presents a comprehensive overview of the application of information technology in agriculture associated with crop management, such as yield prediction, water management, and climate factors based on recent research. Furthermore, this paper presents a soil-based Machine learning comparative analytical framework (SMLF) to predict crop yield production. Evaluates the influence of soil characteristics and climate factors to identify the crop yield prediction class label (High, Low, Medium). Simultaneously this framework presents a comparative analysis of the benchmark classifier to trained and labels the corpus of crop data set for High, low, medium yield prediction.

Suggested Citation

  • Kalpana Jain & Naveen Choudhary, 2022. "Comparative analysis of machine learning techniques for predicting production capability of crop yield," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 583-593, March.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01543-8
    DOI: 10.1007/s13198-021-01543-8
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    References listed on IDEAS

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    1. Paudel, Dilli & Boogaard, Hendrik & de Wit, Allard & Janssen, Sander & Osinga, Sjoukje & Pylianidis, Christos & Athanasiadis, Ioannis N., 2021. "Machine learning for large-scale crop yield forecasting," Agricultural Systems, Elsevier, vol. 187(C).
    2. Gardner, A.S. & Maclean, I.M.D. & Gaston, K.J. & Bütikofer, L., 2021. "Forecasting future crop suitability with microclimate data," Agricultural Systems, Elsevier, vol. 190(C).
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