Regression Analysis of Rice Data for Yield Prediction Using Python Programming Language

The interdisciplinary domain Data Science exists ubiquitously for helping to filter out status of the passive data existing over the internet through analytics techniques on Big Data. In fact, it is intricate procedure of exploring different data set to disclose facts including hidden pattern, unidentified correlations and market trend that could assist organizations make business verdicts by predicting. A number of experts are working on vegetables and fruits yield prediction, the analysis of rice yield prediction using regression analysis with Python language is presented in this paper. The rice data of District Larkana is collected from Agriculture Statistic Department, Islamabad with three factors: Area under Cultivation, Production and Yield. The linear regression technique is applied to calculate the relationship between the Area under Cultivation (Independent) and its effect on Yield (Dependent). The positive, moderate and significant relationship is observed between the dependent and independent variables. This study can helps to researchers for knowing the worth of analytics techniques for prediction of harvest.

[1]  Haedong Lee,et al.  Development of yield prediction system based on real-time agricultural meteorological information , 2014, 16th International Conference on Advanced Communication Technology.

[2]  Vijander Singh,et al.  An Analysis of Big Data Analytics , 2021 .

[3]  Irfana Noor Memon,et al.  Analysis of Rice Profitability and Marketing Chain: A Case Study of District Sukkur Sindh Pakistan , 2015 .

[4]  Eshtiak Ahmed,et al.  An Optimization Approach to Improve Classification Performance in Cancer and Diabetes Prediction , 2019, 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE).

[5]  Salah Sukkarieh,et al.  Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review , 2018, Comput. Electron. Agric..

[6]  A. Colantoni,et al.  Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs , 2019, Processes.

[7]  K. K. K. Singh,et al.  Development of a rice yield prediction system over Bhubaneswar, India: combination of extended range forecast and CERES‐rice model , 2015 .

[8]  V. Sellam,et al.  Prediction of Crop Yield using Regression Analysis , 2016 .

[9]  R. Ghadge,et al.  Prediction of Crop Yield using Machine Learning , 2018 .

[10]  M. Usman Contribution of Agriculture Sector in the GDP Growth Rate of Pakistan , 2016 .

[11]  Inbal Becker-Reshef,et al.  Rice yield estimation using Landsat ETM+ Data , 2015 .

[12]  T. Iizumi,et al.  Global crop yield forecasting using seasonal climate information from a multi-model ensemble , 2018, Climate Services.