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A Survey on Data Mining Techniques in Agriculture


Affiliations
1 Dept. of Computer Science, Karpagam University, Coimbatore, India
2 Dept. of Computer Application, Karpagam University, Coimbatore, India
 

Objective: To study about different data mining methods utilized for detecting plant diseases, soil moisture and crop growth monitoring.

Methods: Different data mining techniques are used in agriculture for detecting crop diseases, soil moisture and crop growth monitoring such as Support Vector Machine (SVM), Artificial Neural Network (ANN) and Regression model.

Findings: The inclusion of modern technologies can enhance the crop production and resolve major issues in traditional farming. The crop production is mainly depends on the availability of arable land and influenced by yields, macro-economic uncertainty and consumption patterns. The actual yield is mostly depends on crop’s genetic potential, amount of sunlight, water and nutrients absorbed by crop, presence of weeds and pests. In addition, the crop production is enhanced by combining crop models with data mining approaches.

Applications/Improvements: Finally, different data mining techniques used in agriculture are compared in order to prove their effectiveness. Hence, the agricultural monitoring system can be enhanced by using data mining techniques.


Keywords

Crop Production, Data Mining Techniques, Crop Diseases, Soil Moisture, Crop Growth Monitoring.
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  • A Survey on Data Mining Techniques in Agriculture

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Authors

D. Sabareeswaran
Dept. of Computer Science, Karpagam University, Coimbatore, India
A. Edwin Robert
Dept. of Computer Application, Karpagam University, Coimbatore, India

Abstract


Objective: To study about different data mining methods utilized for detecting plant diseases, soil moisture and crop growth monitoring.

Methods: Different data mining techniques are used in agriculture for detecting crop diseases, soil moisture and crop growth monitoring such as Support Vector Machine (SVM), Artificial Neural Network (ANN) and Regression model.

Findings: The inclusion of modern technologies can enhance the crop production and resolve major issues in traditional farming. The crop production is mainly depends on the availability of arable land and influenced by yields, macro-economic uncertainty and consumption patterns. The actual yield is mostly depends on crop’s genetic potential, amount of sunlight, water and nutrients absorbed by crop, presence of weeds and pests. In addition, the crop production is enhanced by combining crop models with data mining approaches.

Applications/Improvements: Finally, different data mining techniques used in agriculture are compared in order to prove their effectiveness. Hence, the agricultural monitoring system can be enhanced by using data mining techniques.


Keywords


Crop Production, Data Mining Techniques, Crop Diseases, Soil Moisture, Crop Growth Monitoring.

References