A Brief survey of Data Mining Techniques Applied to Agricultural Data

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.

Investigating Factors that Influence Rice Yields of Bangladesh using Data Warehousing, Machine Learning, and Visualization

“…vi. Using Weka to perform clustering, we can not only cluster the data but also visualize it in many different and often very useful ways to extract maximum knowledge from the data points [17]. vii.…”

“…vii. Even though clustering is supposed to be unsupervised learning, Weka enables the use of machine learning by enabling its clustering algorithms to be trained by splitting the input data into training and test sets [17].…”

Prediction of Tea Production in Kenya Using Clustering and Association Rule Mining Techniques

“…According to Khan and Singh, market based data analysis is the most common application of the association rule, but however, this technique can be applied in other data-sets whereby the researcher wishes to outset new patterns [7]. Essentially, the association mining rule exist in the form X implies Y, where X and Y are collection of variables in a dataset and the intersection of Y and X is null [1]. When the support and confidence are greater than or equal to the pre-defined threshold Supmin and Confmin, the association rule is considered to be a valid rule Apriori algorithm: There are different algorithms that have been developed to enhance association rule data mining technique.…”

“…Specifically, association mining has proved to be very useful specifically in business field in the discovery of purchase patterns, sales patterns and the association between different commodities, and objects [6]. Owing to this, it is clear that association mining can greatly aid in discovering new knowledge for the agricultural data set and aid in boosting production and profitability of agriculture based ventures [1]. Geetha asserts that data collected form agricultural surveys regarding crop production, geographical conditions, soil and cultivation can be mined to establish any regularity in that data and which can be used to predict future aspects [3].…”

“…Owing to this, data mining has emerged to be an area of interest that has attracted a significant number of researches and experimentations in an effort to enhance efficiency and productivity in various disciplines. Data mining can be defined as a process that entails establishing previously unknown, nontrivial and hidden information in a larger dataset [1]. The established relation-ships, associations and patterns within the data can aid in providing useful information [2].…”