Prediction of Sugarcane Diseases using Data Mining Techniques

被引：0
|
作者：
Beulah, R. [1 ]
Punithavalli, M. [1 ]
机构：
[1] Bharathiar Univ, Dept Comp Applicat, Coimbatore, Tamil Nadu, India
来源：
2016 IEEE INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTER APPLICATIONS (ICACA) | 2016年
关键词：
DTM; Random Forest Model; Crop Production; Agrarian Sector; Yield Predictio;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Indian agriculture is a toughest profession with the unpredictability of climate and weather conditions that occur every year. India recently launched INSAT 3DR, an ISRO satellite which gives a clear picture of forecasting rainfall and weather conditions to improve its support to millions of Farmers. Although these facilities are available, the crop yield in affected by the unpredicted diseases which damage the crop. This document presents a succinct study of sugarcane disease calculation by Decision Tree Model (DTM) method and Random Forest method for India. These Data mining techniques give us a better solution to this problem, which can be applied to increase the sugarcane yield prediction.
引用
收藏
页码：393 / 396
页数：4
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