ABSTRACT
The population of our nation has reached over 1.35 billion and the population increase indicates a need for further development in several sectors, including agriculture, to make food available for all. Today, plant diseases are the prime cause of economic casualties in the agricultural sector globally. Identification of plant diseases is an important first step in the protection of plants. In the last decade, considerable research has been done to develop new technical optical methodologies for plant disease detection. Sensors can help humans to control the environment from a remote location. Deep learning and machine learning are integrated to create a system with symbiotic interaction, in which environmental real-time crop data are monitored with the help of Internet-managed systems. The present work describes imaging sensor mechanisms for numerous agriculture applications such as plant epidemic disease control and prediction of situations leading to the spread of epidemic disease. The software architecture can manage numerous models for plant disease and similar applications of precision agriculture. Recent advances in data management are moving modern agriculture towards sustainable smart farming in what has been called Smart Society 5.0.
