Prediction of Sugarcane Yields from Field Records using Regression Modeling
Shivani S. Kale1, Preeti S. Patil2
1Shivani S. Kale, Research Scholar, Department of Computer Science and Engineering, VTU Belgaum, India.
2Dr. Preeti S. Patil, Head and Professor, Department of Information Technology, D.Y. Patil College of Engineering Akurdi, Pune, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 1603-1606 | Volume-8 Issue-4, November 2019. | Retrieval Number: C4174098319/2019©BEIESP | DOI: 10.35940/ijrte.C4174.118419
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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: Prediction Of Sugarcane Crop Yield Benefits The Farmer To Get Best Possible Decision Regarding Sugarcane Crop Cultivation. The Purpose Of This Work Is To Identify Possible Relationship Between N, P, K Fertilizer, Water Resource And Planting Densities. The Algorithm Used Is Multiple Regression. The Paper Focuses On The Generation Of Multiple Regression Models For The Dataset Of Sugarcane Crop For Season Adasali, Suru And Preseasonal Method. The Intercept And Slope For Variables Are Calculated And Equation For Each Model Is Generated. Sample Of N,P,K And Other Are Considered For A Period Of 7 Years From 2012 To 2018. Data Of Experimentation Is Collected For Arid Region I.E. Pandharpur, Maharashtra State.
Keywords: Prediction, Crop Yield, Regression Analysis.
Scope of the Article: Regression and Prediction.