Soil Classification and Fertilizer Recommendation using WEKA

Transcription

1 Soil Classification and Fertilizer Recommendation using WEKA Suman 1, Bharat Bhushan Naib 2 1 Research Scholar, CSE, P.D.M. College of Engineering, Bahadurgarh, Haryana (India) sumansehrawat121@gmail.com 2 Assistant Professor, CSE, P.D.M. College of Engineering, Bahadurgarh, Haryana (India) bharatnaib@gmail.com Abstract Agriculture forms the backbone of any country economy, since a large population lives in rural areas and they directly or indirectly dependent on agriculture for living. The use of standard statistical analysis techniques is both time consuming and expensive. Agricultural research has been profited by technical advances such as automation, data mining. Data mining in agriculture is a novel research field of computer science. Efficient techniques can be developed for solving complex data sets using data mining to improve the accuracy and effectiveness of classification of large soil data sets. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. A soil test is the analysis of a soil sample to determine nutrient content, composition and other characteristics. Tests are usually performed to measure fertility and indicate deficiencies that need to be remedied. In this research, soil dataset containing soil test results has been used to apply various classification techniques in data mining. Soil classification deals with the categorization of soil into different soil classes as very low, low, medium, high, and very high on the basis of % of nutrient found in the soil and on the basis of these classes fertilizer is recommended for a soil sample. Keywords: Soil Dataset, Classification, Recommendation, Agriculture. 1. Introduction Agriculture was the key development in the rise of sedentary human civilization, whereby farming of domesticated species created food surpluses that nurtured the development of civilization. The study of agriculture is known as agricultural science. Soil may be defined as a thin layer of earth s crust which serves as a natural medium for the growth of plants. A study of soil profile supplemented by physical, chemical and biological properties of the soil will give full picture of soil fertility and productivity. Soil 142 can be classified on the basis of taxonomy, ph, liming, nutrients, and organic matters. Data mining plays a crucial role in the Agricultural research [2]. It focuses on classification and clustering of soil using various techniques like artificial neural networks, decision trees, genetic algorithms, nearest neighbor s method etc. WEKA is helpful in learning the basic concepts of data mining where we can apply different options and analyze the output that is being produced[5][16]. This paper is organized as follows. Section 2 includes an analysis of soil fertility using data mining techniques. Section 3 includes the results using WEKA. Section 4 includes the recommendation of the appropriate fertilizer based on fertility level. Section 5 include conclusion. 2. An Analysis of Soil Fertility Using Data Mining Techniques This section include the collection of soil dataset which can be helpful for the classification of soil into different fertility classes using data mining techniques on the basis of percentage of nutrients found in the soil. 2.1 Soil Dataset An intimate knowledge of the soil is pre-requisite for agricultural planning and development programs. The soil dataset is collected as a part of survey and this data is obtained from field sampling. The study of physical, chemical and biological properties of the soil sample will give the knowledge about soil fertility, productivity and deficiencies that need to be remedied. For the WEKA tool the data sets need to

2 be in the ARFF format. The data sets used for the tests come from the soil testing laboratory bahadurgarh, Jhajjar with the help of Dr. Jain Singh Maan. Based on the field and laboratory data collected for each soil sample contain 10 attribute which are needed for plant growth are shown in table. Field N P K S Fe Cu Zn B ph EC Table 1 : Attribute Description 2.2 Soil Classification Description Nitrogen, ppm Phosphorous, ppm Potassium, ppm Sulfur, ppm Iron, ppm Copper, ppm Zinc, ppm Boron, ppm ph value of soil Electrical Conductivity, mmhos/cm Soil classification deals with the categorization of soil into different soil fertility classes as very low, low, medium, high, and very high on the basis of % of nutrient found in the soil. Data mining algorithms Naïve Bayes, K- Nearest neighbour,c4.5 and cla used for classification of soil are as follows: Naïve Bayes Bayesian Classifiers are statistical classifiers based on bayes theorem. Bayesian classification shows high accuracy and speed when applied to large data bases. In this classifier it is assume that the effect of an attribute value on a given class is independent of the values of the other attributes. This assumption is called class conditional independence. This classification can predict the probability that a given tuple belongs to a particular class [6][7][8] K- Nearest Neighbour (KNN) K-Nearest neighbor algorithm (KNN) is one of the supervised learning algorithms. It is based on a distance function for pairs of observations, such as the Euclidean distance or Cosine. Nearest Neighbor search also known as proximity search, similarity search or closest point search is an optimization problem for finding closest points in metric spaces. In this paradigm, k nearest neighbors of a training data is computed first. Then the similarities of one sample from testing data to the k nearest neighbors are aggregatedaccording to the class of the neighbors and the testing sample is assigned to the most similar class [10][11] J48 (C4.5) J48 is an open source Java implementation of the C4.5 algorithm in the Weka data mining tool. C4.5 is a program that creates a decision tree based on a set of labeled input data. This algorithm was developed by Ross Quinlan. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier ( C4.5 (J48), Wikipedia).At each node of the tree, C4.5 chooses one attribute of the data that most effectively splits its set of samples into subsets enriched in one class or the other. Its criterion is the normalized information gain that results from choosing an attribute for splitting the data. The attribute with the highest normalized information gain is chosen to make the decision [9][1] J48 Classification 1. A flow-chart-like tree structure Internal node denotes a test on an attribute. Branch represents an outcome of the test. Leaf nodes represent class labels or class distribution. 2. Decision tree generation consists of two Phases: Tree construction: At start, all the training examples are at the root and partition examples recursively based on selected attributes. Tree pruning : Identify and remove branches that reflect noise or outliers 3. Use of decision tree: Classifying an unknown sample Test the attribute values of the sample against the decision tree [12]. 143

3 Classifier Table 2: Comparison of Accuracy Average Accuracy Rate Naïve Bayes 77% K-Nearest Neighbour 70% J48 85% Proposed Algorithm Proposed algorithm performs the clustering as well as the classification. Firstly the data is clustered by using the K-means Clustering then the linear regression is applied to classify the clusters and the data elements within the clusters. This process can be explained by the following algorithm: 1. Input Data set. 2. Create Number of Clusters, initially 2 clusters are created. 3. Determine the centroid of the Clusters. 4. Determine Distance of objects to centroid using Figure 1: Soil Dataset The dataset is opened using the WEKA. 5. Group the Objects based on minimum distance. 6. If any object move group then go to step Centroid of Clusters act as input for linear classification, linear classification is done by where x is the input and y is the output. a0,a1 are the constants. 8. After classifying all the clusters, classify the elements within the clusters using the linear classification. Figure 2: Open dataset Using WEKA 3.2 Soil Classification Using Various Classifiers Now apply the classification using the classify tab and classify the soil dataset using J48 classifier. 3. Results 3.1 Soil Dataset The soil dataset has 10 attributes and a total 49 instances of soil samples and the dataset prepared in Excel is saved into.csv file to allow them to be applied to WEKA. Figure 3: J48 Tree Soil is classified by using J48 classifier into 5 fertility classes very low, low, medium, high, very high.j48 classifier out of 49 instances classify

4 instances correctly and 5 instances incorrectly in 0.06 sec. Table 3: Comparison of Correctly and Incorrectly Classified Instances Algorithm Correctly Classified Incorrectly Classified Time(in seconds) J Cla Figure 4: Classification by J48 The clustering can also be applied by using the cluster tab. Figure 5: Simple K-mean Clustering using WEKA After clustering of attributes soil is classified into 5 fertility classes very low, low, medium, high, very high. This algorithm out of 49 instances classifies 47 instances correctly and 2 instances incorrectly in 0.03 sec. Figure 6: Classification of Clusters of Attributes The classification applied using the proposed algorithm and J48 algorithm. The table compares the results. The table denotes that the proposed algorithm increases the accuracy as the correctly classified instance are increased and time for classification remains stable. 4. Fertilizer Recommendation This section include that after classification of soil into different fertility classes expert determine what are the deficiencies in the soil, which type of fertilizer should be used for that type of soil and which type of crop is best in that particular type of soil. This facilitates the optimum growth and obtaining the yield potential of the crop. Now fertilizer can be recommended according to fertility level. Soil can be classified on the basis of ph values. When ph value of soil is less than 4.5 then the soil level is very strongly acidic. Scientists recommend that this type of soil is too acidic for most of the crops. To improve its ph it is required to add liming material that has a minimum concentration of 9% Mg. When ph value is in between 4.5 to 5.2 then it is denoted as strongly acidic which also not good for many crops thus to improve its ph liming material having minimum concentration of 9% Mg is recommended. ph value between 5.3 to 6.0 is moderately acidic to improve the ph value of such soil liming material with 3.6% to 9% is necessary. When soil is considered as slightly acidic when ph value is in between 6.1 to 6.9 which is satisfactory for almost crops. But as the ph value increases to the range of 7.0 to 7.5 then soil becomes slightly alkaline which is considered as optimum for most of the crops. As the value of ph increases the alkalinity of soil also increases. When ph value is in between the range of 7.6 to 8.2 the level of soil is moderately alkaline. To drop this type of soils ph gypsum or sulfur are added as it is too alkaline for many crops. Gypsum or sulfur is again recommended when the soil is strongly alkaline with the ph value of 8.6 to 9.0. When ph value reaches up to the value of 9.0 or high then soil becomes highly 145

5 alkaline which is not suitable to many crops to grow. So we recommend adding liming material having minimum concentration of 9% Mg to the ph ranging up to 6.0. While ph value is less than 7.0 but above 6.0 then recommended solution is to add liming material having concentration of 3.6% to 9.0% of Mg. and for the value of ph up to the range of 7.1 to 9.0, gypsum or sulfur are recommended to add in the soil for making it suitable for the crops. Figure 7: Fertilizer Recommendation 5. Conclusion The research undertaken showed that data mining has advantages and can be easily applied to the soil data set to establish patterns in the data. The application of the WEKA data mining platform provided an easy and quick method for the cluster analysis. The platform provides a number of clustering algorithms that can be used for different tasks. This research aims at analysis of soil dataset using data mining techniques. It focuses on classification of soil using various algorithms into different fertility classes and compare the correctly and incorrectly classified instances. On the basis of fertility classes expert can determine what are the deficiencies in the soil and which fertilizer should be used to overcome that deficiency. Analysis Using Classification Techniques and Soil Attribute Prediction [2] Prof. Chandrakanth. Biradar, Chatura S Nigudgi An Statistical Based Agriculture Data Analysis International Journal of Emerging Technology and Advanced Engineering, ISSN: , Volume 2, Issue 9, September [3] Latika Sharma, Nitu Mehta, Data Mining Techniques: A Tool For Knowledge Management System In Agriculture, International Journal Of Scientific & Technology Research Volume 1, Issue 5, ISSN: , June [4] Joyce Jackson, Data mining : A conceptual overview, Communications of the Association for Information Systems Volume 8, ,2002. [5] WEKA, Wikipedia, March [6] Professor Tom Fomby, Naïve Bayes Classifier, April [7] Professor Tom Fomby, Naïve Bayes Classifier, April [8] Naïve Bayes, Wikipedia, March [9] C4.5 (J48), Wikipedia, March [10] Saravanan Thirumuruganathan, A detailed introduction to K-Nearest Neighbor algorithm, may [11] K-Nearest Neighbor, Wikipedia, March [12] Kaushik H. Raviya, Biren Gajjar, Performance Evaluation of Different Data Mining Classification Algorithm Using WEKA, Paripex - Indian Journal Of Research, ISSN , Volume : 2, Issue : 1, January [13] M. S. Chen, J. Han, P. S. Yu. "Data mining: An overview from a database perspective",ieee Trans. Knowledge and Data Engineering, 8: , 1996 [14] [15] m [16] [17] Ramesh Vamanan, K.Ramar Classification Of Agricultural Land Soils A Data Mining Approach, International Journal on Computer Science and Engineering (IJCSE) Issn Vol. 3 No. 1 Jan References [1] Jay Gholap, Anurag Ingole, Jayesh Gohil, Shailesh Gargade, Vahida Attar Soil Data 146