Abstract:

Agricultural resource data include quantities, text, symbols, charts, graphs or other analog inputs that describe agricultural resources. Agricultural resource data yield agricultural resource information. The Xinjiang Production and Construction Corporations are vigorously promoting the study and construction of applications using agricultural big data. Traditional methods for collecting and integrating agricultural data reveal problems such as inconsistent collection standards, poor data quality, information fragmentation, and low fluidity. A set of economic, feasible and efficient methods for agricultural data collection and integration is urgently needed. This study reviewed the research progress on agricultural data resources in China, particularly research on methods for collecting and integrating agricultural resource data. On the basis of systematic observation and analysis of existing agricultural resource data from the Xinjiang Production and Construction Corps, important concerns and necessary functions were identified. Through this study, agricultural data collection and integration methods were divided into a technical indicator specification module, an agricultural resource collection module, a data quality inspection module, a heterogeneous data conversion module, a data classification coding module, a data management module, a decision support module, and an agricultural resource sharing module. An agricultural resources data collection and integration method model was established and the Xinjiang Corps’ agricultural resources integration and sharing platform was built. The quantity, quality, and effectiveness of agricultural resource data are related to the development foundation of agricultural big data. On the basis of progress in the study of data collection and integration methods, this paper describes preliminary collection and integration of dispersed and unique agricultural resource data. This research will continue to identify ways to improve and streamline data collection and integration practices. The goal is an economical and effective working procedure that effectively prepares data for mining, and reveals relationships among different agricultural resource data elements.

Key words: agricultural resources, data integration, methods study, informatization, virtual technology, data acquisition, scientific data acquisition