The inspection of leaf categorization system is a very important subject in the field of Botany. A classifier of leaf with high accuracy will also bring a lot of fun to people's lives. However, because of the complex background of leaves, the similarity between the different species of leaves, and the differrnces among the same species of leaves, there are still some advance in the recognition of leaf images and their exposer to various diseases. Due to this complication it is often difficult to use the appropriate Pesticides. Based on Inception-v3 model of TensorFlow platform, a methodology is projected to identify the leaf and classify the type of diseases that the leaf has been affected by, using the available dataset a neural network is trained to meet the required application and detect the type of disease and displays the possible medicines.

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The adoption of cloud environment for various application uses has led to security and privacy concern of user’s data. To protect user data and privacy on such platform is an area of concern. Many cryptography strategy has been presented to provide secure sharing of resource on cloud platform.  These methods tries to achieve a secure authentication strategy to realize feature such as self-blindable access tickets, group signatures, anonymous access tickets, minimal disclosure of tickets and revocation but each one varies in realization of these features. Each feature requires different cryptography mechanism for realization. Due to this it induces computation complexity which affects the deployment of these models in practical application. Most of these techniques are designed for a particular application environment and adopt public key cryptography which incurs high cost due to computation complexity. To address these issues this work present an secure and efficient privacy preserving of mining data on public cloud platform by adopting party and key based authentication strategy. The proposed SCPPDM (Secure Cloud Privacy Preserving Data Mining) is deployed on Microsoft azure cloud platform. Experiment is conducted to evaluate computation complexity. The outcome shows the proposed model achieves significant performance interm of computation overhead and cost.