Smart Agricultural Crop Prediction Using Machine Learning

Authors

  • G. S. Jayadeva Professor, Department of Electronics and Communications Engineering, B.M.S. Institute of Technology and Management, Bangalore, India
  • S. Shanmugasundaram Student, Department of Electronics and Communications Engineering, B.M.S. Institute of Technology and Management, Bangalore, India
  • N. Sharath Student, Department of Electronics and Communications Engineering, B.M.S. Institute of Technology and Management, Bangalore, India
  • C. Sanju Student, Department of Electronics and Communications Engineering, B.M.S. Institute of Technology and Management, Bangalore, India
  • S. M. Sidhalinga Student, Department of Electronics and Communications Engineering, B.M.S. Institute of Technology and Management, Bangalore, India

Keywords:

Big data analytics, crop yield, knn algorithm, Machine learning, profit, soil weather conditions.

Abstract

Indian economy is mainly dependent on farmer’s progress, making good profit in the agriculture field, technology plays an important role. Getting higher yields and improved quality of final product a system based on machine learning is proposed. In this system analyzing, quality of soil, rain pattern, weather and temperature, the farmers are suggested best crops and its required fertilizers as a solution by which farmers will get more profit on growing system suggested crop. This system is designed as a web application which uses big data analytics, prediction analysis and other techniques to predict the most suitable and profitable crop and its required fertilizers, predicts yield per hectare and value of crop based on current market price taking into consideration of current weather and soil conditions. Thus, farmers will benefit by using our system which will improve crop productivity and profit of farmers.

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Published

21-07-2021

How to Cite

[1]
G. S. Jayadeva, S. Shanmugasundaram, N. Sharath, C. Sanju, and S. M. Sidhalinga, “Smart Agricultural Crop Prediction Using Machine Learning”, IJRAMT, vol. 2, no. 7, pp. 144–147, Jul. 2021.

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Articles