National Conference

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    AN ANALYSIS ON CROP YIELD PREDICTION USING DATA MINING TECHNIQUES
    (Karpagam Academy of Higher Education, 2019-09-27) S, Kavitha; G, Sangeetha
    In day to day life the requirement of food is increasing at rapid rate and hence the farmers, government and researchers are using several techniques in agriculture for the improvement in production. Plants are usually affected by a many pests and diseases. In the process of resolving agricultural issues the concepts of data mining plays a fundamental part. Research in agriculture is increasing due to development of technologies and forth coming challenges [1]. In improving the general growth of a country the plant disease detection has an important place. Diseases in plants, production and loss can be predicted with the help of data mining approaches like classification. The future trends in agricultural processes can be forecasted with the Data mining techniques. Generally the damages were examined by using classifiers namely SVM , K-Nearest Neighbor, Decision Tree, Random Forest, Naive Bayes and so on. [13].
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    A SURVEY ON AGRICULTURAL FERTILIZER TO IMPROVE THE FARMERS PRODUCTIVITY
    (Karpagam Academy of Higher Education, 2020-02-28) Anushya Devi T.S
    Agriculture is an essential factor for human life. Every country depends on farming and agriculture. Because of the Globalization, the agricultural trends have been developed excessively in modern times. Agricultural have been affected by various factors, it may happen because on unknowing factor about fertilizer quantity for land. For this, fertilizer in agriculture and data mining uses techniques to apply specific principles of data and decides it to when and how much fertilizers to be used in a particular area or land. For this factor data mining concept is the process of finding or discovering the pattern to recognize the fertilizer quantity to get more progress and improve the farmer’s productivity. To improve the productivity this content gives us examine of various techniques of data mining approaches.