CLOUD-BASED DECISION SUPPORT AND AUTOMATION FOR PRECISION AGRICULTURE

Hongfei Hou

A decision support system (DSS) compiles data from various data sources with its knowledge-base to provide assistances for business and organization in the decision-making process. DSS has been widely incorporated as the center processor in the medical diagnosis, business management, etc. Existing DSSs work for only specific aspects of operations and don’t have mechanisms built-in to validate inputted data. Thus, inappropriate decisions could be made if data contains errors. It is challenging to improve their limited functionalities and extensibility, and to eliminate errors and overhead caused by lack of validation mechanisms. Recently, cloud computing has been applied in DSS. Cloud computing makes DSS more scalable and reliable, but at the same time, it introduces some significant challenges such as: (1) how can a Cloud-based DSS support various data which could be in different formats, and/or from different sources? (2) how can a Cloud-based DSS accommodate and support various types of operations, and/or operations with different configurations? (3) how can a Cloud-based DSS directly control the diversified field devices properly? (4) how to maintain the security and keep the scalability and flexibility while migrated into Cloud-based DSS? In this dissertation, an extensible Cloud-based DSS framework is proposed. It consists of 4 major components to address these issues: (1) a component containing meta-model-based data acquisition and integration modules which can accept data in different formats; (2) an adaptive software architecture in which modules can be adopted and reconfigured on-the-fly; (3) a software-defined control component which enables a DSS to control various field devices through the unified software-defined interfaces; and (4) a component which self-monitors modules’ and/or devices’ behaviors. The proposed framework was implemented in Ruby on Rail and deployed onto Amazon Web Services (AWS). We were porting it to a Java, JavaScript, and Angular system. In this research, we included tests, experiments, and case studies in two fields: Precision Agriculture and Hydropower Biological Evaluation for characterizing hydraulic conditions and impacts of Hydro-Structures on fish. Descriptions of modules implemented in both fields as well as analysis of the security of the proposed framework are presented in this dissertation.

CLOUD-BASED DECISION SUPPORT AND AUTOMATION FOR PRECISION AGRICULTURE

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