CROP YIELD PREDICTION AND SOIL DATA ANALYSIS USING DATA MINING TECHNIQUES IN KRISHNAGIRI DISTRICT

2018 ◽  
Vol 06 (08) ◽  
pp. 49-55
Author(s):  
K. Samundeeswari ◽  
K. Srinivasan
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Crop Yield ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Using Data

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. 

A Survey on Crop Yield Prediction using Data Mining

2018 ◽  
Vol 65 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Myth ra.N ◽  
◽  
Velayu dham.A ◽  
Sham ila.E.S ◽  
Pavith ra.M
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Yield Prediction ◽  
Using Data

scholarly journals Paddy Yield Prediction Model Using Data Mining Techniques

NCICCNDA ◽  
2018 ◽  
Author(s):  
Insha Sirur ◽  
Karthik B ◽  
Sharath P ◽  
Mohan Kumari M ◽  
Rumana Anjum
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Using Data ◽  
Paddy Yield

scholarly journals Soil Data Analysis and Crop Yield Prediction in Data Mining using R – Programming

2020 ◽  
Vol 9 (3) ◽  
pp. 1857-1860
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Decision Tree ◽  
Crop Yield ◽  
Climatic Condition ◽  
Research Work ◽  
Yield Prediction ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
R Programming

Data mining is better choices in emerging research filed- soil data analysis. crop yield prediction is an important issue for selecting the crop. earlier prediction of crop is done by the experience of farmer 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. data mining techniques is the better choice for predicting the crop. the analysis of soil plays an important role in agricultural filed. soil fertility prediction is one of the very important factors in agriculture this research work implements to predict yield of crop, decision tree algorithm is used to find yield. the aim of this research to pinpoint the accuracy and to finding the yield of the crop using decision tree and c 4.5 algorithm is used to predict the yield of crop using rprogramming and also to find range of magnesium found in the collected soil data set. this prediction will be very useful for the farmer to predict the crop yield for cultivation

scholarly journals Knn based Crop and Fertilizer Prediction

2020 ◽  
Vol 9 (4) ◽  
pp. 1453-1456
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Crop Yield ◽  
Accurate Result ◽  
Machine Learning Algorithms ◽  
Yield Prediction ◽  
Prediction Problem ◽  
Environmental Statistics ◽  
Classification And Regression ◽  
Using Data

India has always been active in agriculture, in fact even in this age of industrialization agriculture and agriculturebased industries continue to be a main source of income for a large percentage of the population. Machine learning and data mining have become, in the present day, are very important mediums when it comes to research in the crop yielding domain. Many a times we come across news on the paper about farmers committing suicide because of crop failures and increase in loans. In preventing such situations, crop yield prediction software can play a very important role. This research is an attempt in proposing a method to predict the success of crop for a particular area by using data on amounts and ratios of different components of soil like nitrogen, potassium, phosphorus and environmental statistics on temperature and weather. Various machine learning algorithms are used to get an accurate result. KNN is used for classification and regression prediction problem. It also attempts in providing a precise output on what fertilizers can be used to better the yield. Through this, therefore, farmers will also be able to predict their profits and final revenues.

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