| Abstract | Data mining assumes an essential part in the dynamic cycle in numerous application territories. Information
mining is essential for data handling and the executives. Plant sicknesses bargain efficiency which impacts public
activity and economy of the country. The viable utilization of horticulture information mining can upgrade yield
creation and give monetary advantage to the rancher and the country. The acknowledgment of the disease is done
truly by noticing and recognizing the microorganisms, are which is for the most part take additional time and is
likewise much expensive with lower precision. In this way, to defeat that there is most ideal decision this is fast
and errorless determination by utilizing a few methods. We can see the side effects of contaminations or illnesses
on the various pieces of the natural products in the plant, leaf, injuries. The point of this article is to recognize
and distinguish the sickness precisely from the picture dataset. In this paper we proposed Enhanced Back
Propagation Neural Network to distinguish the sickness on the natural products dataset. Our exploratory
outcomes express that the proposed arrangement can essentially uphold exact discovery and programmed
distinguishing proof of natural product illnesses.
Keywords [ Data mining, Recognition, Back Propagation, accurate detection]
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