Smart Yield Accuracy Prediction using Linear Regression and Collaborative Filtration
V Prithvi Ram1, Rajeshwari S.B2, Jagadish S Kalliman3
1V Prithvi Ram, Department of Information Science and Engineering, MSRIT, Bangalore, Karnataka, India.
2Rajeshwari S B, Assistant Professor, Department of Information Science and Engineering, MSRIT, Bangalore, Karnataka, India.
3Dr Jagadish S Kallimani, Associate Professor, Department of Computer Science and Engineering, MSRIT, Bangalore, Karnataka, India
Manuscript received on 11 August 2019. | Revised Manuscript received on 19 August 2019. | Manuscript published on 30 September 2019. | PP: 664-670 | Volume-8 Issue-3 September 2019 | Retrieval Number: B2657078219/19©BEIESP | DOI: 10.35940/ijrte.B2657.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: India is mainly based on farming. Agriculture is the main source of economy in India, but the farmers are suffering with many problems such as lack of crops yield, lack of water, soil fertility etc. To address those issues this recommendation system is proposed, and it significantly influences the crops yields. The need for the accessible data on the accomplishment for getting crops in good yields are investigated. To accomplish that, real-time data are collected from the farmers from different places of Karnataka. In this paper linear regression and collaborative filtering are used, and results are compared to draw an inference for more accurate recommendation system.
Keywords: Crop Yields, Collaborative Filtering, Linear Regression.
Scope of the Article: Filtering