Crop Yield Prediction using Regression Model
Shikha Ujjainia1, Pratima Gautam2, S. Veenadhari3
1Shikha Ujjainia*, Department of Computer Science and Application, Rabindranath Tagore University, Bhopal, India.
2Pratima Gautam, Dean (CSIT), Rabindranath Tagore University, Bhopal, India.
3S. Veenadhari, Associate Professor (CSE), Rabindranath Tagore University, Bhopal, India.
Manuscript received on July 16, 2020. | Revised Manuscript received on July 29, 2020. | Manuscript published on August 10, 2020. | PP: 269-273 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J74910891020 | DOI: 10.35940/ijitee.J7491.0891020
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Abstract: This research is done to find out the production dependability of crop with various physical circumstances. The prediction can also be done of a crop yield by using the model of regression and it is mainly discussed in this paper. Machine learning is an emerging research area in Agriculture, particularly in crop yield analysis and prediction. There are some complex data which are tough to decode or find by everyone, the strategies of machine learning can be used in this scenario and automatically the valuable underlining pattern can be accessed. Various complex decision-making activities can be performed when the feature of machine learning will enable the knowledge and patterns which are unseen about any problem. The future events can also be predicted. In the growing season as possible, a farmer is focused on conceptualizing how much yield they except. Like many other regions, the amount of agricultural data is increasing at the daily source. This paper aims to predict crop yield on the collected agricultural dataset. The regression analysis model is used to test the accuracy and effective predictions of the rice crop yield in India. Linear regression is used to establish a relationship between various environmental variables like temperature, rainfall, etc and the crop yield. It is important to measure the possible production of rate of crop and the farmers will be benefitted by the result of this prediction. As financial impact is attached of the farmers with the yield production, the research will support them to avoid any loss. The accuracy of the prediction through regression model is also observed in this research paper.
Keywords: Machine learning, Regression model, Linear regression, Yield prediction.