Outlier Detection in Agriculture Domain: Application and Techniques

Outliers are those values that do not comply with the general behavior of the existing data. Outliers vary quantitatively from rest of the data, according to any outlier-selection algorithm. Normal data values or objects follow a common generating mechanism, whereas the abnormal objects deviated from that mechanism and it seems that they have been generated from some different mechanisms. These abnormal data objects are referred as “Outliers”. In this paper, authors have tried to explore various applications and techniques of outlier detection. Further, an algorithm for detecting the outliers in agriculture domain has been proposed and its implementation through hand-coded ETL tool, AGRETL, has been discussed. The results show the significant improvement, when the algorithm was validated on the real-time dataset.

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