Knn based Crop and Fertilizer Prediction
Prakash U.M1, Sristika Bora2, Abhishek Gautam3, K.R. Gokul Anand4
1Sristika Bora*, Dept. of CSE, SRM Institute of Science and Technology, Chennai, India.
2Prakash U.M, Assistant Professor, Dept. of CSE, SRM Institute of Science and Technology, Chennai, India.
3Abhishek Gautam, Dept. of CSE, SRM Institute of Science and Technology, Chennai, India.
4K.R. Gokul Anand, Assistant Professor, Dept. of CSE, SRM Institute of Science and Technology, Chennai, India
Manuscript received on March 30, 2020. | Revised Manuscript received on April 05, 2020. | Manuscript published on April 30, 2020. | PP: 1453-1456 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7436049420/2020©BEIESP | DOI: 10.35940/ijeat.D7436.049420
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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 has always been active in agriculture, in fact even in this age of industrialization agriculture and agriculturebased industries continue to be a main source of income for a large percentage of the population. Machine learning and data mining have become, in the present day, are very important mediums when it comes to research in the crop yielding domain. Many a times we come across news on the paper about farmers committing suicide because of crop failures and increase in loans. In preventing such situations, crop yield prediction software can play a very important role. This research is an attempt in proposing a method to predict the success of crop for a particular area by using data on amounts and ratios of different components of soil like nitrogen, potassium, phosphorus and environmental statistics on temperature and weather. Various machine learning algorithms are used to get an accurate result. KNN is used for classification and regression prediction problem. It also attempts in providing a precise output on what fertilizers can be used to better the yield. Through this, therefore, farmers will also be able to predict their profits and final revenues.