In the Indian Economy, agriculture plays a main role, therefore prior detection of plant diseases will aid in maximizing the productivity of the crops thereby adding to the economy’s augmentation. To predict the plant diseases, manual identification is used earlier but it requires vast manpower and wide knowledge about plants. Multi disease models and pest prediction can be automated using image processing techniques. This paper shows an overview of various image processing techniques to obtain and organize diseases in the plant. Developing contamination, supplement lack, dry season, and so on are the reasons in light of which plants are inclined to the various sicknesses. Illnesses can be found on the root, stem, branches, leaves, blossoms, and organic products. Diseases in plants are the main production and financial losses also decrease in agricultural product quality and quantity. To propose a proper solution for relating illness, recognizable proof, and arrangement of infections is significant. We reviewed a simple plat leaf disease detection system that would ease progressions in agriculture. The survey on various classification techniques presented in this paper for plant leaf diseases. An Automatic finding of plant diseases is significant to regularly find out the disease signs as soon as they emerge on the mounting phase.

Assigning a misusability weight to a given dataset is strongly related to the way the data is presented (e.g., tabular data, structured or free text) and is domain specific. Therefore, one measure of misusability weight cannot fit all types of data in every domain but it gives a fair idea on how to proceed to handle sensitive data. Previous approaches such as M-score models that consider number of entities, anonymity levels, number of properties and values of properties to estimate misusability value for a data record has better efficiency in deducting record sensitivities. Quality of data, Quantity data, and the distinguishing attributes are vital factors that can influence Mscore. Combined with record ranking and knowledge models prior Approaches used one domain expert for deducting sensitive information. But for better performance and accuracy we propose to use the effect of combining knowledge from several experts (e.g., ensemble of knowledge models). Also we plan to extend the computations of sensitivity level of sensitive attributes to be objectively obtained by using machine learning techniques such as SVM classifier along with expert scoring models. This approach particularly fits the sensitive parameter values to the customer value based on customer activity which is far more efficient compared to face value specification with human involvement. A practical implementation of the proposed system validates our claim. INTRODUCTION: