Analyzing Agricultural Crop Production and their Uncertainty Using Linear Regression and Fuzzy Logic

Om Prakash Singh, Bijay Kumar Mandal, Sunil Kumar
2021 IARJSET  
Our nation India is an agrarian country and its economy based upon agricultural crops production. The share of agriculture in GDP increased to 19.9 percent in 2020-21 and almost fifty percent of total manpower utilized their efforts in this sector. The uncertain whether, climate changes and water storage with traditional farming trends and improper irrigation facilities are directly affect the crop productivity. All such parameters make the environment of uncertainty regarding crops production.
more » ... On the other hand, accurate and timely predictions of crop production are backbone of the policy maker regarding import-export, demand-supply, marketing, pricing and distributions to balance the socioeconomic frame. The uncertainty and its prediction tend to be complex phenomena. The primary resources of uncertainty are randomness and fuzziness. The randomness deals with general uncertainties while fuzzy logic suitable for the complex phenomena. The statistical regression methodology is used traditionally for such complex predictions. In series of smart notations, the smart forming is necessity of day. Hence there is a requirement to develop qualitative and statistically sound prediction of crop yields with machine learning handling large amount of data. The present works focus on investigation of various used machine learning algorithms for their suitability in crop yields perdition and finally proposed an approach based on linear regression and fuzzy logic in big data computing paradigm for accurate and timely predictions of crop production. IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License 313 PROPOSED APPROACH In this article the machine learning based linear regression with fuzzy logic for accurate and timely prediction of crops production is discussed from model selection and data acquisition to its validation. The proposed approach framework consists of fuzzy logic based controller-Fig1, Fuzzy Linear regression-Fig2 and Framework-Fig3. The factors or parameters that influence the crop yields are uncertain by nature so the probability will be computed by using linear regression. Then fuzzy logic will be applied for various parameters. IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License 314 6. CONCLUSION There is necessity of employed the machine learning techniques in agriculture for finding the many secret knowledge by analyzing such big data where data are structured and unstructured with insufficient relationship between dataset. The various literatures show that now the different machine learning techniques used for predictions of crop yield but this proposed approach capable for accurate and timely predictions of crop yield production, empowering the former community and balancing the socio-economic frame. The fuzzy logic and regression derived linear fuzzy regression is most suitable alternative of statistical regression model to handle the diverse qualitative and quantitative parameters as a real time application. We are in our best effort to implement this approach with Python. 7.
doi:10.17148/iarjset.2021.8855 fatcat:ghes6dm3zbanlb2bkrvibsh7k4