Application of Data Analysis and Soft Computation to Model the Need of Crop Insurance for the Indian Farmers


  • Bhavesh Agarwal Jain Class of 2023, Dhirubhai Ambani International School, Bandra - Kurla Complex, Bandra (East), Mumbai - 400098, Maharashtra, India.
  • Reetu Jain On My Own Technology Pvt Ltd, Mumbai, India


Crop insurance, Exploratory data analysis, Correlational analysis, Threshold value computation, Machine learning model


A very high level of uncertainty is associated with agriculture in the form of natural, social and human-related actions. Farmers incur heavy losses whenever their farmlands are affected. Crop insurance is the answer to such losses that existed as an institutional response to the nature-induced risk. Although crop insurance is advantageous, there are some drawbacks in implementation because of which the farmers are not willing to opt for it. Hence, there is a demand to model the need of crop insurance for Indian Farmers. For this purpose, the paper is divided into two parts. The first part involves applying exploratory data analysis (EDA) to correlate the factors with the farmers' responses. A correlational analysis is also conducted to study the relationship between different factors. The second part involves the application of three machine learning (ML) algorithms, namely, Logistic Regression (LR), Random Forest (RF) and Gradient Boost classifier (GB) to meet the aims and objectives of the paper. The performance of the three ML models is scrutinised based on their accuracy and predictive capability. The ML model with the best performance is chosen to determine the threshold value below which there is a high likelihood of a farmer opting for crop insurance. The strength of the proposed approach is its practical applicability.



Bansal, Sanjay Kumar, and Pratik Dwivedi. "Impact of Agriculture on NPA & Indian Economy. Journal Global Values Vol. XI Sp. Issue (Jan.2020).

Vyas, V. S., and Surjit Singh. "Crop insurance in India: Scope for improvement." Economic and Political Weekly (2006): 4585-4594.

INCCA Indian Network for Climate Change Assessment (2010) Climate change and India: a 4X4 assessment - a sectorial and regional analysis for 2030s.

Ray, L. K., Goel, N. K., & Arora, M. (2019). Trend analysis and change point detection of temperature over parts of India. Theoretical and Applied Climatology, 138(1), 153-167.

Rohini, P., Rajeevan, M., & Srivastava, A. K. (2016). On the variability and increasing trends of heat waves over India. Scientific reports, 6(1), 1-9.

Sharma, S., & Mujumdar, P. (2017). Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India. Scientific reports, 7(1), 1-9.

Hussain, S. A.I. and Hatibaruah, D., 2015. Modeling for prediction of tomato yield and its deviation using artificial neural networks. International Journal of Engineering Trends and Technology, 29, pp.102-108.

Hardaker, J. B., R. B. M. Huirne, and J. R. Anderson. "Coping with risk in agriculture, CAB International, Wallingford." (1997).

Nelson, Gerald C., Mark W. Rosegrant, Jawoo Koo, Richard Robertson, Timothy Sulser, Tingju Zhu, Claudia Ringler et al. Climate change: Impact on agriculture and costs of adaptation. Vol. 21. Intl Food Policy Res Inst, 2009.

Shagun. Climate crisis has cost India 5 million hectares of crop in 2021. Down To Earth (2021).

Mahapatra, Richard. Drought forever. Down To Earth (2021) (2014).

Arora, Anchal, and Devesh Birwal. "Natural calamities, crop losses and coping strategies: an economic analysis from Odisha." Indian Journal of Agricultural Economics 72, no. 3 (2017): 385-395.

Gharde, Yogita, P. K. Singh, R. P. Dubey, and P. K. Gupta. "Assessment of yield and economic losses in agriculture due to weeds in India." Crop Protection 107 (2018): 12-18.

Parvathamma, G. L. "Farmers suicide and response of the Government in India-An Analysis." IOSR Journal of Economics and Finance 7, no. 3 (2016): 1-6.

Carleton, T. A. (2017). Crop-damaging temperatures increase suicide rates in India. Proceedings of the National Academy of Sciences, 114(33), 8746-8751.

Raghavalu, M. V. "Farmers' Suicide Deaths in India: Can it be Controlled?" Economic Affairs 58, no. 4 (2013): 441.

Majumder, Madhuparna. "Literature Review on “Correlates Associated with Farmer's suicide”." (2021).


Datta, P., & Behera, B. (2022). Climate Change and Indian Agriculture: A Systematic Review of Farmers’ Perception, Adaptation, and Transformation. Environmental Challenges, 100543.

Swain, Mamata. "Crop insurance for adaptation to climate change in India." (2014).

Singh, Gurdev. Crop insurance in India. Ahmedabad: Indian Institute of Management, 2010.

Mahul, Olivier. "Hedging price risk in the presence of crop yield and revenue insurance." European Review of Agricultural Economics 30, no. 2 (2003): 217-239.

Gupta, Rakesh. "Agrarian Crisis and Distress: A Wakeup Call." Prajnan 45, no. 1 (2016).

Mohan, Rohini. As crops fall, insurance must rise to curb farmer suicides. The Economic Times (2015).

Diefenbach, Thomas. "Are case studies more than sophisticated storytelling?: Methodological problems of qualitative empirical research mainly based on semi-structured interviews." Quality & Quantity 43, no. 6 (2009): 875-894.

Sukamolson, Suphat. "Fundamentals of quantitative research." Language Institute Chulalongkorn University 1, no. 3 (2007): 1-20.

Johnston, Melissa P. "Secondary data analysis: A method of which the time has come." Qualitative and quantitative methods in libraries 3, no. 3 (2017): 619-626.

Myatt, Glenn J. Making sense of data: a practical guide to exploratory data analysis and data mining. John Wiley & Sons, 2007.

Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter business technology journal, 31(2), 47-53.

Gogas, P., & Papadimitriou, T. (2021). Machine learning in economics and finance. Computational Economics, 57(1), 1-4.

Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.

Akyildirim, E., Goncu, A., & Sensoy, A. (2021). Prediction of cryptocurrency returns using machine learning. Annals of Operations Research, 297(1), 3-36.

Bertinetto, L., Henriques, J. F., Torr, P. H., & Vedaldi, A. (2018). Meta-learning with differentiable closed-form solvers. arXiv preprint arXiv:1805.08136.

Gracia, S., Olivito, J., Resano, J., Martin-del-Brio, B., de Alfonso, M., & Álvarez, E. (2021). Improving accuracy on wave height estimation through machine learning techniques. Ocean Engineering, 236, 108699.

Marković, T., Ivanović, S., & Todorović, S. (2013). Reduction in revenue volatility in maise production applying the indirect-index insurance. Economics of Agriculture, 60(3), 445-454.

Medhi, T., Hussain, S. A. I., Roy, B. S., & Saha, S. C. (2021). An intelligent multi-objective framework for optimising friction-stir welding process parameters. Applied Soft Computing, 104, 107190.

Ghosh, R. K., Gupta, S., Singh, V., & Ward, P. S. (2021). Demand for crop insurance in developing countries: new evidence from India. Journal of agricultural economics, 72(1), 293-320.

Lopes, L. L. (1987). Between hope and fear: The psychology of risk. In Advances in experimental social psychology (Vol. 20, pp. 255-295). Academic Press.

Goodwin, B. K., & Smith, V. H. (1995). The economics of crop insurance and disaster aid. American Enterprise Institute.

Nguyen, V. T., Jung, K., & Gupta, V. (2021). Examining data visualisation pitfalls in scientific publications. Visual Computing for Industry, Biomedicine, and Art, 4(1), 1-15.

Tanujaya, B., Mumu, J., & Margono, G. (2017). The Relationship between Higher Order Thinking Skills and Academic Performance of Student in Mathematics Instruction. International Education Studies, 10(11), 78-85.




How to Cite

Jain, B. A., & Jain, R. (2022). Application of Data Analysis and Soft Computation to Model the Need of Crop Insurance for the Indian Farmers. iJournals:International Journal of Software & Hardware Research in Engineering ISSN:2347-4890, 10(8). Retrieved from

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