A Novel Neuro Fuzzy Classification Technique For Soil Data Mining

1070 words - 5 pages

Data mining with agricultural soil databases is a relatively young research area. In agricultural field, the determination of soil category mainly depends on the atmospheric conditions and different soil characteristics. Classification as an essential data mining technique used to develop models describing different soil classes. Such analysis can present us with a complete understanding of various soil databases at large. In our study, we proposed a novel Neuro-fuzzy classification based technique and applied it to large soil databases to find out significant relationships. We used our technique to three benchmark data sets from the UCI machine learning repository for soil categorization ...view middle of the document...

Agricultural research fields have used various techniques of data analysis including, decision trees, genetic programming, statistical machine learning and other analysis methods [7]. This paper outlines research work involving new data mining techniques to improve the effectiveness and classification accuracy of large soil databases.
Agriculture or farming builds the backbone of any country economy, since a large population lives in rural areas and is mostly dependent on agriculture for a living. Income from agriculture forms the main source for the farming community. The fundamental requirements for farming are water resources and capital to buy seeds, fertilizers, pesticides, labour etc. Most farmers collect the required funds by compromising on other necessary expenditures, and when it is still insufficient they resort to credit from sources like banks and private financial organizations. In such a situation, the repayment is purely dependent on the success of the crop production. If the crop production remains unsuccessful even at one time because of many reasons, like adverse weather condition; soil type; inappropriate, unreasonable, and untimely application of both fertilizers and pesticides; impure seeds and pesticides etc., then the farmer will have to face havoc crisis causing severe stress [8]. Besides this, the crop production depends on many factors like soil pattern, crop type, and weather condition. Because of the deficiency of crop harvesting information and professional guidance, many farmers do not gain a beneficial solution.
General knowledge of soil in the world adds up from the soil survey analysis, which is the process of determining the soil types or other attributes of the ground cover over a particular geographical area, and mapping them for the knowledge workers and researchers comprehend and use [6]. Major data for soil survey are developed by field sampling and held up by remote sensing. Classification as an important data mining technique can be used for soil survey analysis so that it could be helpful for crop harvesting. Thus, soil classification as a form of soil survey analysis can guide the farmers in the attainment of the crop yield. That is why such analysis is very much important in the agricultural research field.
Artificial Neural Network (ANN) [5], [6], [7] is a popular data modeling tool that can perform intelligent tasks similar to the human brain. It is well-known for high precision and high learning ability...

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