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Data Mining And Analysis Of Our Agriculture Based On The Decision Tree
Abstract
The decision tree is one of the common modeling methods to classify. Firstly, this paper introduces the concept of classification and the method of the decision tree. Then, this paper analyses the data of rural labor, arable land area and the gross output value of agriculture about 30 cities of China based on the decision tree, and adopts clustering analysis method to discretize continuous data during the process of data mining in order to subjectivity comparing to the traditional classification methods. Finally, generating the decision tree of our agriculture, thereby gaining the spatial classification rules and analyzing the rules.
... An analysis of it is performed using OLAP operations. Paper [24] uses decision tree for analysis of for gross output value of agriculture for more than 30 cities of China. [25] aims to analyse the agricultural data of India using data mining algorithms. ...
Agriculture is the main source and backbone of Indian Economy and plays a vital role in individual life. In the total Gross Domestic Product (GDP) agriculture nearly contributes sixteen percent and for increasing foreign exchange it contributes nearly ten percent to the total country exports. As the population continuously increasing and to manage the livelihood of the country there’s requires a proper utilization and management of agriculture products. Data mining is a better technique and best choice in predicting the accurate prices of the agriculture based on previous data. In this work various data mining algorithms are applied on the dataset to predict the future prices of the agriculture products. Keywords: Agriculture, Data-Mining, Prediction.
... Machine learning model like Support Vector Machine (SVM) and Relevance Vector Machine (RVM) is used which makes prediction on soil moisture n days ahead.The research by M. Paul, S. K. Vishwakarma and A. Verma [8] is about prediction of crop yield by transforming into classification rule where naïve Bayes and K nearest neighbors methods are used. The research by S. Dimitriadis and C. Goumopoulos [9] uses machine learning techniques which automatically extracts new knowledge in the form of decision rules. The result of the study is the creation of effective set of decision rules that are used to predict plant's state and prevent unpleasant impact of water stress in plants. ...
Indian economy mainly depends on the agriculture, which contributes a major part in the growth and development of the nation. This is directly connected to the standard of life of farmers which covers more than 40 percent of the country population. Looking at the current situation of agriculture in India, agricultural productivity in India is not competitive to the world standards. For increasing the crop productivity farmers are forced to use more fertilizers which will end in health risk for the consumers. Lack of a proper knowledge of the effective usage of fertilizers and the changing soil nutrient values are the major problems for a farmer apart from the loss due to climate change and so many other factors. To address the problem , we propose to build an application model 'Prediction On Fertilizer Management for Crop Productivity' which enables the farmers to understand and effectively utilize their money with effective methods of seasonal crops production by providing recent and intimate information about which has to be produced, soil suitability, its nutrient values and soil moisture level which pasteurization management methods, how to apply the fertilizers in larger areas, what amount of water must be given, how to develop a model maps to the current scenario of farm areas. Crop Yield Production is basically an aggregation of crop and fertilizer recommendation, soil analysis, and crop yield calculations based on recent market scenario. Through our model we are revising and correcting the existing system with predictive analytics of the usage of effective fertilizers with less health risk.
... The object of data mining can be any type of data source or database such as text, multimedia data, etc. The common techniques include: artificial neural networks, decision trees (Gao and Ren, 2009), genetic algorithms (Deng and Wang, 2016;Indu et al., 2021), nearest neighbour algorithms, rule derivation (Vignesh and Vinutha, 2019), etc. In the field of agriculture, the technology of data mining at this stage revolves around the continuous development of data collection technology. ...
... 2) Decision Tree: This data mining technique has a tree-like structure where the top is a root and each leaf has an attribute value to be analysed while each branch represents the possible output. The structure is simple but reliable for processing multi-dimensional data [22] widely used for prediction and classification [23][24][25]. The challenge faced when using this method is the repetition process of optimum tree structure analysis based on certain conditions affecting application performance. ...
This research aims to find out what data mining techniques are effectively implemented in museums and what application trends are currently being used to improve museum performance towards modern museums based on intelligent system technology. The review was carried out on a number of articles found in journals and proceedings in the 2004-2020 period. It is found that the majority of data mining techniques are implemented in museum virtual guide applications, recommender systems, collection clustering and classification system, and visitor behaviour prediction application. Data classification, clustering, and prediction technique commonly used for museum application. Collections with historical and artistic value contain a lot of knowledge making data mining an important technique to be included in various applications in museums so that they can have an impact on the achievement of museum goals not only in the fields of education and culture but also economics and business.
... Gao and Ren [9] used decision trees for agricultural analysis of 30 cities. They first used clustering techniques and then generate a decision tree for this purpose. ...
... In the survey of the China the researchers analyzed the data of rural labor, arable land area and the gross output value of agriculture of about 30 provinces and cities (such as Beijing, Tianjin, hebei, Shanxi, Neimenggu, Liaoning, jilin, heilngjiang and so on) on the basis of collected data. The researchers have generated decision tree of agriculture and have found the classification rules and analysis of these rules [1]. ...
Agriculture arguably the backbone of India's economy is highly dependent on spatial and temporal distribution of monsoon rainfalls. This paper represents the concept of clustering and use the centroid based technique k-means algorithm. The data of foodgrain production of Kharif and Rabi during the last 10 years has been obtained from [6]. Use the k-means algorithm on the gathered data to generate the clusters of Indian states which gives the similar productions of crops. The states in India have been clustered into 3 clusters using k-means clustering algorithm. The experiments done in SPSS indicate that some states fall into the same cluster as per the produce of Rabi and some fall into the same cluster as per the produce of Kharif crops.
... In the literature is possible to find several documents related to mining and use of meteorological data in different countries [5] [6]. The purpose of this study was to apply a data mining algorithm in a database collected by radiosondes attached to weather balloons (Fig. 1). ...
Seed Selection is a very challenging job because for a selection of a seed multifarious parameters are to be taken under consideration. Also seed analysis require a prediction of which seed is suitable which needs a great accuracy as there are numerous things to be taken into account like soil type, ph of soil, nutrient content of soil, elevation of land, weather of the area, etc. Several algorithms have been devised from time to time but each of the methods differs in their own way. The algorithms, which are discussed, are K-Means Algorithm, K-Nearest Neighbor Algorithm, Naïve Bayes Classifier, Decision Tree, Regression Model, etc. Data mining techniques can overcome this challenging job.
The agribusiness volatility is related to the uncertainty of the environment, rising demand, falling prices and new technologies. However, generation of agriculture data has increased over past years and can be used for a growing number of applications of data mining techniques in agriculture. The multidisciplinary approach of integrating computer science with agriculture will support the necessary decisions to be taken in order to mitigate risks and maximize profits. The present study analyzes different methods of regression applied in the study case of grapes production forecast. The selected methods were multivariate linear regression, regression trees, lasso and random forest. Their performance were compared against the predictions obtained by the company through the mean squared error and the coefficient of variation. The four regression methods used obtained better predictive results than the method used by the company with statistical significance < 0.5%.
Decision tree is a well known approach for classification in data mining. C4.5 and Classification and Regression Trees (CART) are two widely used decision tree algorithms for classification. The main drawback of C4.5 algorithm is that, it is biased towards attributes with more values while CART algorithm produces misclassification errors when the domain of the target attribute is very large. In view of these limitations, this paper presents a modified decision tree algorithm. The C4.5, CART and the proposed classifier are trained using data set containing soil samples by considering optimal soil parameters namely pH (power of Hydrogen), Ec (Electrical Conductivity) and ESP (Exchangeable Sodium Percentage). The model is tested with test data set of soil samples. The test proves that the modified decision tree algorithm has higher classification accuracy when compared to C4.5 and CART algorithms.
Data mining is a method used by geographic information systems for ex-bring useful information for understanding and discovering the nature, characteristics, patterns and relationships within spa-tial data, in order to maximize the knowledge to use them trees decision as an effective model pre-diction. In this regard, the following report presents a theoretical discussion about the decision trees and their implementation with spatial data mining, providing a series of definitions, characteristics, advantages with the intention of giving the reader a conceptual perspective that implementation.
Generation of agriculture data has increased over past years to judge impact of agriculture parameters to make action plan and to examine agriculture productivity. This data is generally spatio – temporal in nature and may have additional dimensions such as agriculture parameters (agriculture land, arable land etc), environmental attributes (Co2 emission etc) and geographical attributes (region, state etc). It's a challenging task to analyze this growing data and generate useful results. Various methods are available to analyze data which makes use of various parameters to generate results. In this paper first we build a multidimensional model of data then apply multidimensional analysis, statistical analysis (as co-relation) on multidimensional model and data mining technique (as association rule mining) on correlated data. Our analysis approach helps us to build model and apply advance techniques like multidimensional data analysis, statistical mining and data mining to extract knowledge from this model. There are various data collecting agencies like World Bank, IMF, Department of Economics and Statistics and lot of private agencies like ORG, AC-Nielsen. We have presented our approach using a case study to analyze agriculture productivity using various agriculture related parameters. We have used data available on World Bank website.
The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.
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