A survey of data mining techniques applied to agriculture

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A brief survey of data mining techniques applied to agricultural data The paper provides 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)

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What are the data mining techniques used in agriculture?

Several data mining techniques such as k-means, k-nearest neighbour, artificial neural networks and support vector machine are implemented for proper functioning and smart growth of agriculture.

What is the best book on data mining in agriculture?

Mr. Abhishek B. Mankar, Mr. Mayur S. Burange, Data Mining – An Evolutionary View of Agriculture, International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 3, Issue 3, March 2014. Hetal Patel, Dharmendra Patel A brief survey of data mining technique applied to agricultural data.

What is data mining and data mining?

Data mining is a task of extracting more and more data from known and existing data (discovery of new data from set of databases). By doing data mining we can able to generate and build meaningful and knowledgeable data. Data mining includes various techniques that implement the use of data mining.

What is the use of predictive data mining?

Predictive data mining technique is used to predict future crop, pesticides and fertilizers to be used, revenue to be generated for proper growth and function of crops in agriculture.

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How is data mining used in agriculture?

Data Mining in smart agriculture are being used mainly for planning soil and water use, monitoring crops health, reducing and optimizing the use of natural resources, limiting the use of pollutants (e.g. pesticides, herbicides), improving the quality of the production etc.


What are the techniques of data mining?

Below are 5 data mining techniques that can help you create optimal results.Classification analysis. This analysis is used to retrieve important and relevant information about data, and metadata. … Association rule learning. … Anomaly or outlier detection. … Clustering analysis. … Regression analysis.


What are the 3 types of data mining?

Types of Data MiningPredictive Data Mining Analysis.Descriptive Data Mining Analysis.


What is agriculture mining?

Data mining in agriculture is a very recent research topic. It consists in the application of data mining techniques to agriculture. Recent technologies are nowadays able to provide a lot of information on agricultural-related activities, which can then be analyzed in order to find important information.


What is data mining techniques PDF?

Data mining is a process of extraction of. useful information and patterns from huge data. It is also called as knowledge discovery process, knowledge mining from data, knowledge extraction or data /pattern analysis.


What is the purpose of data mining techniques?

Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more.


What are the data mining applications?

Data Mining ApplicationsFinancial Data Analysis.Retail Industry.Telecommunication Industry.Biological Data Analysis.Other Scientific Applications.Intrusion Detection.


What are types of data in data mining?

Let’s discuss what type of data can be mined:Flat Files.Relational Databases.DataWarehouse.Transactional Databases.Multimedia Databases.Spatial Databases.Time Series Databases.World Wide Web(WWW)


What are the four major types of data mining tools?

What is Data Mining Tool?Rapid Miner. It is developed by Rapid Miner company; hence the name of this tool is a rapid miner. … Orange. It is open-source software written in python language. … Weka. The University of Waikato develops weka. … KNIME. … Sisense. … Apache Mahout. … SSDT. … Rattle.More items…


Why is mining important to agriculture?

Agriculture also needs mined products such as potash for soil improvement. Mining requires upgraded or new infrastructure, for transport and export, and may open up new areas, and these can benefit agriculture and other aspects of a country’s economic development.


What is agricultural data?

Agricultural data is a subsection of Industry data. It can be used to understand crop production and to cater to the growing number of people in the world.


How does mining affect the agriculture?

Mining affects farming in different ways including loss of farm lands, competition for limited farm labor; increase cost of other farm inputs and environmental pollution which adversely affect the quality of farming in the mining area.


What is data mining in agriculture?

Abstract–Data mining is a fast emerging and highly rising research oriented field in agriculture for formulating and analysing various conditions on crop yield. In this paper our focus is on studying and experimenting the applications of data mining techniques in agricultural field. Data mining plays a significant and unique role for making decision on several issues related to agriculture field. Data mining in agriculture can provide help in predicting yield, forecasting weather and rainfall, quality of seed and soil, production of crops. Predictive data mining technique is used to predict future crop, pesticides and fertilizers to be used, revenue to be generated for proper growth and function of crops in agriculture. Several data mining techniques such as k- means (KM),k-nearest neighbour (KNN), artificial neural network (ANN), support vector machine (SVM) are implemented to solve and help in searching various different ways to improve the growth of agriculture. Each data mining technique has its own different way of visualizing various problems and leading to give us a proper solution for every causing problem in agriculture. In this paper we gained to achieve proper knowledge and correct information of each data mining technique thereby using them in all related issues. It summarises the whole data by combining and executing all the techniques mentioned along with by implementing some new technique thereby improving the way of agriculture planning leading to proper agricultural growth.


Why is the productivity of agriculture so low?

The productivity of agriculture is very low as compared with others, so as the demand of food is increasing daily, the researchers, farmers, agricultural scientists and government are trying to. put extra effort and techniques for more production. And as a result, the agricultural data increases day by day.


What is regression learning?

Regression is learning a function that maps a data item to a real-valued prediction variable. The different applications of regression are predicting the amount of biomass present in a forest, estimating the probability of patient will survive or not on the set of his diagnostic tests, predicting consumer demand for a new product. [9] Here the model is trained to predict a continuous target. Regression tasks are often treated as classification tasks with quantitative class tag. The methods for prediction are Nonlinear Regression (NLR) and Linear Regression (LR).


What is clustering in statistics?

In clustering, the focus is on finding a partition of data records into clusters such that the points within each cluster are close to one another. Clustering groups the data instances into subsets in such a manner that similar instances are assembled together, while dissimilar instances belong to diverse groups. Since the aim of clustering is to find out a new set of categories, the latest groups are of interest in themselves, and their assessment is intrinsic. [7] There is no prior knowledge about data. The different clustering methods are Hierarchical Methods (HM), Partitioning Methods (PM), Density-based Methods (DBM), Model -based Clustering Methods (MBCM), Grid-based Methods and Soft-computing Methods [fuzzy, neural network based], Squared Error—Based Clustering (Vector Quantization), network data and Clustering graph [8]


What is classification and prediction?

Classification and prediction are two forms of data analysis that can be used to extract models describing important data classes or to predict future data trends. It is a process in which a model learns to predict a class label from a set of training data which can then be used to predict discrete class labels on new samples. To maximize the predictive accuracy obtained by the classification model when classifying examples in the test set unseen during training is one of the major goals of classification algorithm. Data mining classification algorithms can follow three different learning approaches: semi-supervised learning, supervised learning and unsupervised learning. The different classification techniques for discovering knowledge are Rule Based Classifiers, Bayesian Networks (BN), Decision Tree (DT), Nearest Neighbour (NN), Artificial Neural Network (ANN), Support Vector Machine (SVM), Rough Sets, Fuzzy Logic, Genetic Algorithm s. [6]


How does data mining help in agriculture?

Use of information technology in agriculture can change the situation of decision making and farmers can yield in better way. Data mining plays a crucial role for decision making on several issues related to agriculture field. It discusses about the role of data mining in the agriculture field and their related work by several authors in context to agriculture domain. It also discusses on different data mining applications in solving the different agricultural problems. This paper integrates the work of various authors in one place so it is useful for researchers to get information of current scenario of data mining techniques and applications in context to agriculture field.


What is association rule mining?

Association rule mining technique is one of the most efficient techniques of data mining to search unseen or desired pattern among the vast amount of data. In this method, the focus is on finding relationships between the different items in a transactional database. Association rules are used to find out elements that co-occur repeatedly within a dataset consisting of many independent selections of elements (such as purchasing transactions), and to discover rules. The simple problem statement is: Given a set of transactions, where each transaction is a set of literals, an association rule is a phrase of the form X => Y, where X and Y are sets of objects. The instinctive meaning of such a rule is that transactions of the database which contain X tend to contain Y. [4] An application of the association rules mining is the market basket analysis, customer segmentation, store layout, catalog design, and telecommunication alarm prediction.


What is a DBSCAN?

DBSCAN is a base algorithm for density based clustering containing large amount of data which has noise and outliers. DBSCAN has two parameters namely Eps and MinPts. However, traditional DBSCAN cannot produce optimal Eps value [ 15 ]. Determination of the optimal Eps value automatically is the one of the most necessary modification for the DBSCAN. Figure 1 briefs the modified approach of the DBSCAN method.


What is data mining in agriculture?

Data mining techniques are necessary approach for accomplishing practical and effective solutions for this problem. Agriculture has been an obvious target for big data. Environmental conditions, variability in soil, input levels, combinations and commodity prices have made it all the more relevant for farmers to use information and get help to make critical farming decisions. This paper focuses on the analysis of the agriculture data and finding optimal parameters to maximize the crop production using data mining techniques like PAM, CLARA, DBSCAN and Multiple Linear Regression. Mining the large amount of existing crop, soil and climatic data, and analysing new, non-experimental data optimizes the production and makes agriculture more resilient to climatic change.


What is partitioning based algorithm?

It is a partitioning based algorithm. It breaks the input data into number of groups. It finds a set of objects called medoids that are centrally located. With the medoids, nearest data points can be calculated and made it as clusters. The algorithm has two phases:


What is open access?

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author (s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.


What is multiple linear regression?

Multiple linear regression is a variant of “linear regression” analysis. This model is built to establish the relationship that exists between one dependent variable and two or more independent variables [ 19 ].For a given dataset where x 1 … x k are independent variables and Y is a dependent variable, the multiple linear regression fits the dataset to the model:


What are the factors that affect agriculture?

Some of the factors on which agriculture is dependent are soil, climate, cultivation, irrigation, fertilizers, temperature, rainfall, harvesting, pesticide weeds and other factors .


Why is historical crop yield important?

Historical crop yield information is also important for supply chain operation of companies engaged in industries. These industries use agricultural products as raw material, livestock, food, animal feed, chemical, poultry, fertilizer, pesticides, seed and paper.

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