Agricultural monitoring and early warning in the era of big data

Abstract

Agricultural information is essential for the World Food Organization, governments, food traders, and management of farms. By providing a powerful new solution, the Big Data era transforms agricultural monitoring and early warning from being model-driven to datadriven. Along with their rapid growth, Big Data and cloud computing technologies provide an innovative means for agricultural monitoring and early warning. Since 2013, CropWatch, a remote-sensing-based global agricultural monitoring system, has gradually introduced various techniques that deal with Big Data, such as cluster analysis, time series analysis, correlation analysis, and spatial and temporal abnormal pattern analysis, into the operational system. Big Data technologies have successfully enhanced the data mining capability of CropWatch and expanded the spatial and temporal coverage of agricultural monitoring and early warning. Big Data has also had a catalytic role in promoting the service-oriented agricultural information cloud service. Big Data has also become an important driving force in upgrading the principles of the CropWatch agricultural monitoring and early warning system. In the future, with the help of Big Data, agricultural monitoring and early warning systems are expected to move toward fully automated monitoring, real-time management, and precise agriculture information service direction. Volunteered geographic information in the Big Data era provides an efficient technique for acquiring Big Data for agricultural monitoring and early warning. Based on the capacity of cross-cutting data mining technology, the diversification of crop-border information services will become the mainstream direction of agricultural information services in the Big Data era. With the use of Big Data technologies, CropWatch will transform into a Big-Data-driven agricultural monitoring and early warning system.