Sugarcane Crop Disease Prediction and Expected Yield Estimation using SVM

  • Shruti Kudagi, Suhas Patil, Mrunal Bewoor

Abstract

Indian economic growth is completely based on agriculture. It shares major percentage in economy. In field of agriculture main threats are climatic changes and temperature. Secondary threats like soil type, nutrients, type of crop. All this plays an important role. One of the essential ways to solve the problem is using branch of Artificial Intelligence (AI) that is Machine Learning (ML). By using machine learning algorithm one can identify the crop yield prediction. In this paper we have proposed solution for crop yield prediction which uses rainfall attributes. Model is web-based which uses previous data  for prediction. In this we have focused on sugarcane crop and disease related to sugarcane for obtaining results. Here we are using different Machine Learning algorithms with their accuracy. Here we are using Support Vector Machine (SVM) which gives better result than other algorithms. This help the mill and farmer to analyze the profit on basis of yield.  This paper focuses on Maharashtra state only. Also predict disease according to place and rainfall and give particular solution to disease.

Keywords: Crop disease,  Crop yield,  Machine learning,  Prediction, SVM

Published
2020-06-06
How to Cite
Shruti Kudagi, Suhas Patil, Mrunal Bewoor. (2020). Sugarcane Crop Disease Prediction and Expected Yield Estimation using SVM. International Journal of Advanced Science and Technology, 29(4s), 2936 -2945. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/22239