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Evaluation of Predictive Data Mining Algorithms in Soil Data Classification for Optimized Crop Recommendation IEEE PROJECT 2018





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Agricultural research has strengthened the optimized economical profit, internationally and is very vast and important field to gain more benefits. However, it can be enhanced by the use of different technological resources, tool, and procedures. Today, the term data mining [1][2]is an interdisciplinary process of analyzing, processing and evaluating the real-world datasets and prediction on the basis of the findings. Our case-based analysis provides empirical evidence that we can use different data mining classification algorithms to classify the dataset of agricultural regions on the basis of soil properties. Additionally, we have investigated the most performing algorithm having powerful prediction accuracy to recommend the best crop for better yield.




UNDERSTANDING OF DATASET

the soil samples that are being used were collected from different fields and the surrounding of Kasur district Punjab, Pakistan. We have acquired the test center data from Soil Fertility Department, Kasur in the form of unstructured and manual format.

The data was collected by surveying different locations on different dates and containing the test samples of soil for different properties. After the acquisition, the digitization of record has been made to convert data into the structured format for further processing.


PREPROCESSING OF DATASET

Soil Dataset consists of different attributes that have complex relationships between dynamic variables. Therefore, before implementing any algorithm the soil distinguished properties must be encountered. We have split the dataset into twodata sets, (i) training and (ii) test data.

(i) Training data, 40% of the dataset (304 instances), will be used as the tuning and validation of our data model and this will formulate the association between the predictive classes. (ii) Test data (456 Instances) will be used for evaluating the strength of our classification model.


CLASSIFICATION

Distinguished properties (i.e. PH level, organic and inorganic matter, texture and temperature, etc.) of soil have made the classification very critical and dynamic in nature. Therefore, we need a robust systematic categorization of soil with objectively efficient and effective algorithms and methods.

Besides, the structural complexity a closer analysis is likely to lead to an improved prediction process that can be helpful in the future.


Existing Sytem

The soil is highly important and subservient organism to run the ecosystem and the importance of soil in agriculture is understandable because that is the basic bedrock of the agricultural industry.

In Pakistan, the soil characterization is a basic component and has the potential to increase the yield per acre, but unfortunately due to not having any appropriate technological resources that are difficult to distinguish and classify the soil so that the suitable crops can be grown at the right location.


Proposed System

Today, the term data mining is an interdisciplinary process of analyzing, processing and evaluating the real-world datasets and prediction on the basis of the findings. Our case-based analysis provides empirical evidence that we can use different data mining classification algorithms to classify the dataset of agricultural regions on the basis of soil properties.

Additionally, we have investigated the most performing algorithm having powerful prediction accuracy to recommend the best crop for better yield


Conclusion




In this study, we have presented the research possibilities for the classification of soil by using well-known classification algorithms as J48, BF Tree, and OneR and Naïve Bayes; in data mining. The experiment was conducted on data instances from Kasur district, Pakistan. We have observed the comparative analysis of these algorithms have the different level of accuracy to determine the effectiveness and efficiency of predictions.

However, the benefits of the better understanding of soils classes can improve the productivity in farming, reduce dependence on fertilizers and create better predictive rules for the recommendation of the increase in yield. In the future, we contrive to create a Soil Management and Recommendation System, which can be utilized effectively by agriculturist and laboratories for Soil Testing. This System will help to recommend a suitable fertilizer and predict for better yield.



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