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Plant diseases, mainly caused by bacteria and fungi, affect crop yield and quality. Detecting disease symptoms at an early stage and promptly is a significant obstacle in safeguarding crops. In developing nations, experts and agronomists commonly opt for visual identification of diseases on vast farms, which incurs both time and monetary expenses. Scientists have suggested diverse deep neural network architectures for recognizing plant ailments. Nevertheless, deep learning algorithms necessitate a vast amount of parameters, which extends the training duration but yields commendable precision. While deep learning and Densenet are widely used in pesticide recommendations. Researchers have suggested diverse deep-learning architectures for detecting agricultural ailments and recommend appropriate pesticides. Test images were diagnosed using an automated Densenet model and the results were verified by plant pathologists. An accuracy of over 92% was achieved in identifying the disease. Our solution is an innovative, scalable and accessible tool for disease management of various crops that can be implemented as a cloud service for farmers and professionals involved sustainable agricultural production.

For the time being, many government or private organizations for recruitment of staff or educational institutions moving towards online based tests. The online examination system is a software application used for conducting examination using computer systems. It helps to the recruitment agency or any govt. or private organizations for conducting any job recruitment examinations transparently. Due to this system results are processed without delay and efficiently evaluated to assess the candidates abilities. But the biggest challenge for online examination system is data integrity, security and privacy. The current system is resolving the privacy issue by providing authentication credentials such as user name, password to the candidates. So that only authorized users with proper credentials can login to the system and attempt the exam. But the data confidentiality and integrity are biggest challenges for the system. As the data stored in system database is in plain text format, hence it may be modified or misused by the internal staff of the organization. This paper presents the frame work for secure storage and management of candidates data using encryption scheme, distributed databases in cloud database system. The proposed framework enhances the data confidentiality, integrity and avoids any cheating by internal staff or third party institutions. This paper conducts experimental work on proposed framework and analyses the results of the system. [ABSTRACT FROM AUTHOR]