Enhanced Approach for Disease Prediction in Sugarcane Crop with the support of advanced machine learning strategies

Abstract

     The paper gives method for crop yield prediction for sugarcane crop, this shows high, low, medium range for yield on the basis of rainfall. It also shows the disease concerned with that locality which will help the farmer to take preventive measures and this will improve the yield quality. The Support Vector Machine algorithms plays important role to predict the yield for sugarcane which gives more accuracy based on rainfall (98.21%). In future there is more scope to work on many different datasets and predict sugarcane yield with plot information in different linguistics and waste management of sugarcane can also be studied. 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 helps 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.