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Crop Type Prediction: A Statistical and Machine Learning Approach

Author

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  • Bikram Pratim Bhuyan

    (School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248006, India
    LISV Laboratory, University of Paris Saclay, 10–12 Avenue of Europe, 78140 Velizy, France
    These authors contributed equally to this work.)

  • Ravi Tomar

    (Persistent Systems, Pune 411016, India
    These authors contributed equally to this work.)

  • T. P. Singh

    (School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248006, India
    These authors contributed equally to this work.)

  • Amar Ramdane Cherif

    (LISV Laboratory, University of Paris Saclay, 10–12 Avenue of Europe, 78140 Velizy, France
    These authors contributed equally to this work.)

Abstract

Farmers’ ability to accurately anticipate crop type is critical to global food production and sustainable smart cities since timely decisions on imports and exports, based on precise forecasts, are crucial to the country’s food security. In India, agriculture and allied sectors constitute the country’s primary source of revenue. Seventy percent of the country’s rural residents are small or marginal agriculture producers. Cereal crops such as rice, wheat, and other pulses make up the bulk of India’s food supply. Regarding cultivation, climate and soil conditions play a vital role. Information is of utmost need in predicting which crop is best suited given the soil and climate. This paper provides a statistical look at the features and indicates the best crop type on the given features in an Indian smart city context. Machine learning algorithms like k-NN, SVM, RF, and GB trees are examined for crop-type prediction. Building an accurate crop forecast system required high accuracy, and the GB tree technique provided that. It outperforms all the classification algorithms with an accuracy of 99.11% and an F1-score of 99.20%.

Suggested Citation

  • Bikram Pratim Bhuyan & Ravi Tomar & T. P. Singh & Amar Ramdane Cherif, 2022. "Crop Type Prediction: A Statistical and Machine Learning Approach," Sustainability, MDPI, vol. 15(1), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:481-:d:1017213
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    References listed on IDEAS

    as
    1. Bikram Pratim Bhuyan & Ravi Tomar & Amar Ramdane Cherif, 2022. "A Systematic Review of Knowledge Representation Techniques in Smart Agriculture (Urban)," Sustainability, MDPI, vol. 14(22), pages 1-36, November.
    2. Naresh Kumar, S. & Aggarwal, P.K., 2013. "Climate change and coconut plantations in India: Impacts and potential adaptation gains," Agricultural Systems, Elsevier, vol. 117(C), pages 45-54.
    3. Samir KC & Marcus Wurzer & Markus Speringer & Wolfgang Lutz, 2018. "Future population and human capital in heterogeneous India," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(33), pages 8328-8333, August.
    4. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Vrontis, Demetris, 2022. "Managing knowledge in Indian Organizations: An empirical investigation to examine the moderating role of jugaad," Journal of Business Research, Elsevier, vol. 141(C), pages 26-39.
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