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Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression

Author

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  • Ekaansh Khosla

    (Indian Institute of Technology (ISM))

  • Ramesh Dharavath

    (Indian Institute of Technology (ISM))

  • Rashmi Priya

    (Indian Institute of Technology (ISM))

Abstract

At the present time, one of the most important sources of survival as well as the most crucial factor in the growth of Indian economy is agriculture. More than 70% of the Indian population is involved in agricultural activities. The crop yield prediction is one of the most desirable yet challenging tasks for every nation. Nowadays, due to the unpredictable climatic changes, farmers are struggling to obtain a good amount of yield from the crops. To feed the increasing population of India, there is a need to incorporate the latest technology and tools in the agricultural sector. This study focuses on the prediction of major kharif crops in Andhra Pradesh’s one of the largest costal districts: Visakhapatnam. As rainfall is the main factor in determining amount of kharif crop production, in this study, first we predict the amount of monsoon rainfall by using modular artificial neural networks (MANNs), and then, we predict the amount of major kharif crops that can be yielded by using the rainfall data and area given to that particular crop by using support vector regression (SVR). By using the methodology of MANNs-SVR, proper agricultural strategies can be made in order to increase the yield of the crops. Comparison with other machine learning algorithms has been done which shows that the proposed methodology outperforms in predicting the instances for kharif crop production.

Suggested Citation

  • Ekaansh Khosla & Ramesh Dharavath & Rashmi Priya, 2020. "Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(6), pages 5687-5708, August.
    • Handle: RePEc:spr:endesu:v:22:y:2020:i:6:d:10.1007_s10668-019-00445-x
    DOI: 10.1007/s10668-019-00445-x
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    References listed on IDEAS

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    1. M. Moriondo & C. Giannakopoulos & M. Bindi, 2011. "Climate change impact assessment: the role of climate extremes in crop yield simulation," Climatic Change, Springer, vol. 104(3), pages 679-701, February.
    2. Doug Howe & Maria Costanzo & Petra Fey & Takashi Gojobori & Linda Hannick & Winston Hide & David P. Hill & Renate Kania & Mary Schaeffer & Susan St Pierre & Simon Twigger & Owen White & Seung Yon Rhee, 2008. "The future of biocuration," Nature, Nature, vol. 455(7209), pages 47-50, September.
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    Cited by:

    1. Che-Hao Chang & Jason Lin & Jia-Wei Chang & Yu-Shun Huang & Ming-Hsin Lai & Yen-Jen Chang, 2024. "Hybrid Deep Neural Networks with Multi-Tasking for Rice Yield Prediction Using Remote Sensing Data," Agriculture, MDPI, vol. 14(4), pages 1-21, March.
    2. Priya Brata Bhoi & Veeresh S. Wali & Deepak Kumar Swain & Kalpana Sharma & Akash Kumar Bhoi & Manlio Bacco & Paolo Barsocchi, 2021. "Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach," Agriculture, MDPI, vol. 11(9), pages 1-27, August.

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