A Brief survey of Data Mining Techniques Applied to Agricultural Data
Hetal Patel,Dharmendra Patel +1 more
TLDR
A brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain and the application of data mining techniques and predictive modeling application in the agriculture field.Abstract:
As with many other sectors the amount of agriculture data based are increasing on a daily basis. However, the application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel research area. In this paper we provide a brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain. The Data Mining techniques applied on Agricultural data include k-means, bi clustering, k nearest neighbor, Neural Networks (NN) Support Vector Machine (SVM), Naive Bayes Classifier and Fuzzy cmeans. As can be seen the appropriateness of data mining techniques is to a certain extent determined by the different types of agricultural data or the problems being addressed. This survey summarize the application of data mining techniques and predictive modeling application in the agriculture field.read more
Citations
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Journal ArticleDOI
Prediction of Crop Yield using Regression Analysis
V. Sellam,E. Poovammal +1 more
TL;DR: In this paper, the authors used regression analysis to analyze the environmental factors and their infliction on crop yield and found that yield is mainly dependent on AR, AUC and FPI.
Proceedings ArticleDOI
A review of the application of data mining techniques for decision making in agriculture
Niketa Gandhi,Leisa Armstrong +1 more
TL;DR: This review proposes that further investigations are needed to understand how artificial neural networks, Bayesian networks, and support vector machines can be used with complex agricultural datasets for crop yield prediction integrating seasonal and spatial factors by using GIS technologies.
Proceedings ArticleDOI
Use of data mining in crop yield prediction
TL;DR: In this paper, the authors have used different classifiers such as J48, LWL, LAD Tree and IBK for prediction and then the performance of each classifier is compared using WEKA tool.
Proceedings ArticleDOI
Designing a Wireless Sensor Network for Precision Agriculture Using Zigbee
TL;DR: The WSN system which is developed in this paper, is used for precision agriculture and the real-time data is based on the several characteristics of weather like temperature, humidity etc.
References
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TL;DR: Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts are illustrated.
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