JIA-2018-09

1928 Yanbo Huang et al. Journal of Integrative Agriculture 2018, 17(9): 1915–1931 sensing big data architecture for remote sensing data supply chain management will be built with the state of the art technology of data management, analytics and visualization. The sharing, security, and privacy requirements of the remote sensing big data supply chain system will be defined and developed accordingly. The temporal dimension of remote sensing big data generates two insight information, historical trend and current status. The historical trends can be derived with the archived data while the current status has to be determined with real-time data acquisition, processing and analysis. Remote sensing for earth observation generates massive volume of data everyday. For the information that has a potential significance, the data have to be collected and aggregated timely and effectively. Therefore, in today’s era, there is a great deal added to real-time remote sensing big data than it was before (Rathore et al. 2015). Rathore et al. (2015) proposed a real-time big data analytical architecture for remote sensing satellite application. Accordingly, it would be expected that a real-time big data analytical architecture for precision agriculture will appear soon. As evolved from artificial neural networks, deep-learning (DL) algorithms have been widely studied and used for machine learning in recently. DL learns and identifies the representative features through a hierarchical structure with the data. Now, DL is being used for remote sensing data analysis from image preprocessing, classification, target recognition, to the advanced semantic feature extraction and image scene understanding (Zhang et al. 2016). Chen el at . (2014) conducted DL-based classification of NASAAirborne Visible/Infrared Imaging Spectrometer hyperspectral data with the hybrid of principle component analysis, DL stacked autocoders, and logictic regression. Basu et al. (2015) proposed a classification framework that extracts features from input images from the National Agricultural Imagery Program dataset in the United States, normalizes the extracted features and feeds the normalized features into a Deep Belief Network for classification. Artificial neural networks have been developed and applied for processing and classification of agricultural remote sensing data (Huang 2009, 2010a). With the development of artificial neural networks in deep learning, agricultural remote sensing will share the results of the studies of deep learning in remote sensing data processing and analysis and develop unique research and development for precision agriculture. Overall, agricultural remote sensing has a number of requirements from big data technology for further development: • Rapid and reliable remote sensing data and other relevant data. • High-efficient organization and management of agricultural remote sensing data. • Capability of global remote sensing acquisition and service. • Rapid location and retrieve of remote sensing data for specific application. • Data processing capability at the scales of global, national, regional and farm. • Standardized agricultural remote sensing data interactive operation and automated retrieve. • Tools of agricultural remote sensing information extraction. • Visual representation of agricultural remote sensing information. To meet the requirements, the following works have to be accomplished in the next few years: • Standardize agricultural remote sensing data acquisition and organizing. • Agricultural information infrastructure building, especially high-speed network environment and high- performance group computing environment. • Agricultural information service systems building with better data analysis capability with improved, faster and complete mining of agricultural remote sensing big data in deeper and broader horizons. • Building of agricultural remote sensing automated processing models and agricultural process simulation models to improve the quality and efficiency of agricultural spatial analysis. • Building of computationally intensive agricultural data platform in highly distributed network environments for coordination of agricultural information services to solve large-scale technical problems. Acknowledgements This research was financially supported by the funding appropriated from USDA-ARS National Program 305 Crop Production and the 948 Program of Ministry of Agriculture of China (2016-X38). Assistance provided by Mr. Ryan H. Poe of USDA ARS was greatly appreciated for acquiring and processing images of USDA ARS. References Anonymous. 2015. The 10 countries most active in space. [2017- 10-25]. http://www.aerospace-technology.com/features/ featurethe-10-countries-most-active-in-space-4744018/ Bastiaanssen W G M, Molden D J, Makin I W. 2000. Remote sensing for irrigated agriculture: Examples from research and possible applications. Agricultural Water Management , 46 , 137–155. Basu S, Ganguly S, Mukhopadhyay S, DiBiano R, Karti M, Nemani R. 2015. DeepSat - A learning framework for