A Survey on Different Data Mining Techniques for Crop Yield Prediction

2019 ◽  
Vol 7 (1) ◽  
pp. 738-744
Author(s):  
R. Beulah
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Yield Prediction ◽  
Data Mining Techniques

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Prognosis for Crop Yield Production by Data Mining Techniques in Agriculture

2019 ◽  
pp. 145-158 ◽  
Author(s):  
Divya Singh ◽  
Dinesh Sharma
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Agricultural Research ◽  
Rapid Development ◽  
Agricultural System ◽  
Yield Prediction ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Yield Production

In agriculture, data mining technique is used for extracting information from a large dataset. The techniques for data mining are used in yield prediction for crop at broader spectrum. Agricultural system is very complex and vast therefore to deal with large data situation is a great factor. Different consultancy, industrial production department, organization related to crops is taking keen interest towards crop yield prediction. Here the focus is on the applicability of data mining techniques in agricultural field. The classification and clustering techniques of data mining are used recently in agriculture field. Data mining technology merged with the rapid development of computer science. This chapter focuses on collecting information and overcome the short comes of manual data handling and prediction of yield results of crop production. Data mining is a prominent agricultural research area for analysis of crop yield. These predictions are a very important in solving agricultural problems for crops.

scholarly journals Soil Data Analysis and Crop Yield Prediction in Data Mining using R-Tool

2020 ◽  
Author(s):  
K. Samundeeswari ◽  
K. Srinivasan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Crop Yield ◽  
Tamil Nadu ◽  
Crop Production ◽  
Experimental Result ◽  
Yield Prediction ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Data Mining Techniques

Background: Crop yield prediction is an important issue for the proper selection of crop for sowing. Earlier prediction of crop is done by the farmer’s experience on a particular type of field and crop. Predicting the crop is done by the farmer’s experience based on the factors like soil types, climatic condition, seasons and weather, rainfall and irrigation facilities. Methods: Data mining techniques is the better choice for predicting the crop. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year’s crop production. This research proposes and implements a system to predict crop yield from soil data. This is achieved by applying Decision Tree Algorithm on agricultural data. The main aim of this research is to pinpoint the accuracy of Decision Tree Algorithm and C 5.0 algorithm which is used to predict the crop yield. Result: This paper presents a brief analysis of Crop yield prediction using data mining technique based decision tree algorithm and C5.0 algorithm for the selected region (Krishnagiri) district of Tamil Nadu in India. The experimental result shows that the proposed work efficiently to determine the accuracy of decision tree algorithm and also to predict the crop yield production using R- Tool.

Sign in / Sign up

Export Citation Format

Share Document