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Free Download Prediction And Enhancement Of Crop Yield By Big Data Analysis Project in Java with source code

Java project     kalai selvi    2020-01-05

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Prediction And Enhancement Of Crop Yield By Big Data Analysis project is a desktop application which is developed in Java platform. This Java project with tutorial and guide for developing a code. Prediction And Enhancement Of Crop Yield By Big Data Analysis is a open source you can Download zip and edit as per you need. If you want more latest Java projects here. This is simple and basic level small project for learning purpose. Also you can modified this system as per your requriments and develop a perfect advance level project. Zip file containing the source code that can be extracted and then imported into Eclipse. This Source code for BE, BTech, MCA, BCA, Engineering, Bs.CS, IT, Software Engineering final year students can submit in college. This script developed by kalai selvi. This desktop application 100% working smooth without any bug. It is developed using hadoop and Database hdfs. This software code helpful in academic projects for final year students. We have a great collection of Java projects.





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About Project



project Name :  Prediction And Enhancement Of Crop Yield By Big Data Analysis
project ID :  2244
Developer Name :  kalai selvi
Upload Date :  2020-01-05
project Platform :  Java
Programming Language :  hadoop
IDE Tool :  Eclipse
project Earning :  kalai selvi Earn Rs.150 from this project.
Database :  hdfs
project Type :  desktop Application
No of project Download :  182
project Total View :  2046
Today Trends :  213
Current Month Trends :  254
Last Month Trends :  196
project Source Code Link :   Download Here (4.0805MB)
project Report Link :   Download here  (0.0239MB)

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Features of the Project

Agriculture is a backbone of Indian economy that is the main income source for most of the population in India. So farmers are always curious about yield prediction. Crop yield depends on various factors like soil, weather, rain, fertilizers and pesticides. Several factors have different impacts on agriculture, which can be quantified using appropriate statistical methodologies. Applying such methodologies and techniques on historical yield of crops, it is possible to obtain information or knowledge which can be helpful to farmers and government organizations for making better decision and policies which lead to increased production. The objective of the work is to compare various data mining techniques which gives the maximum accuracy. Data mining is only the way that assists to convert huge data into technologies and make them available to the farmers. The huge amount of data can be utilized to mine nugget of knowledge that can be useful for farmers and decision makers to take effective and prompt decision. In this paper one of major parameter which is used to increase crop production is considered; that is soil. Different classification algorithms are applied to soil data set to predict its fertility. This paper focuses on classification of soil fertility rate using K-Means, Random Tree, and Apriori.


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