JIA-2018-09

1926 Yanbo Huang et al. Journal of Integrative Agriculture 2018, 17(9): 1915–1931 monitoring of grass growth and productivity was conducted in China (Xu et al. 2008, 2013). Besides, global agricultural remote sensing monitoring was conducted through GEO (Group on Earth Observations) and APEC (Asia-Pacific Economic Cooperation) networks to cover rice, corn and wheat in USA, Canada, Australia, Philippine, Thailand, and Vietnam (Chen et al. 2011; Ren et al. 2015). 4.2. Agricultural remote sensing systems operated for precision agriculture in Mississippi Delta Global and national agricultural remote sensing monitoring is a coordination of satellite remote sensing and ground-based remote sensing. Remote sensing for regional and local farm monitoring and control is a coordination of airborne remote sensing and ground on-the-go proximal remote sensing. For precision agriculture, LARS plays a critical role in providing prescription data for controlling variable-rate operations. UAVs provide a unique platform for remote sensing at very low altitudes with very high resolution images over crop fields to be supplement to airborne remote sensing on manned aircraft and ground-based proximal remote sensing. The Mississippi Delta is the section in the northwest of the state of Mississippi in the United States. The section lies between the Mississippi River and the Yazoo River. Agriculture is the backbone of the Mississippi Delta’s economy. This area has many advantages for massive commercial crop production with plain topography, extensive surface and ground water resources and nutrient-rich soils. Major crops produced in the Mississippi Delta are cotton, soybean, corn and rice. Major agricultural companies such as Monsanto Company (St. Louis, MO, USA), Syngenta AG (Basel, Switzerland) and Dow AgroSciences LLC (Indianapolis, IN, USA) have local office for research and farm consultation or collaborated with local companies to farming enhancement for sustainable development of agriculture in this area. In the middle of 1960s, the USDA ARS established the Jamie Whitten Delta States Research Center at Stoneville, Mississippi. The center consists of seven research units, with scientists conducting basic and applied research in the areas of biological control, crop entomology, crop genetics, cotton ginning, pest biological control, crop genomics and bioinformatics, and crop production systems to aim at agricultural problems of the Mid South area of the United States centered in the Mississippi Delta. The Mississippi Delta is well-suited for mechanized agriculture for large-scale crop production in typical of large flood plains with the area ranging from nearly flat to undulating, gentle slopes (Snipes et al. 2005). As a major research task, scientists in the Crop Production Systems Research Unit have been conducting research on developing techniques of precision agriculture on the basis of mechanized agriculture. The research has focused on two research farms located in the area of Stoneville, Mississippi (A: 33.441803°, –90.886169° and B: 33.446753°, –90.872211°, respectively) with the areas of 65 and 49 ha, respectively (Fig. 5) for engineering development and technical evaluation of aerial application technology, aerial remote sensing systems building and methods development for crop growth monitoring and stress detection, and system evaluation for aerial variable- rate application with prescription from remote sensing monitoring. The valuable results and information from the researches in the farms have been extended to support Mississippi Delta agricultural development. Since 2008 three terabyte data from multispectral, hyperspectral and thermal imaging sensors have been accumulated with an average increase of 30 gigabytes per year for various studies of crop stress from herbicide damage (Huang et al. 2010b; Huang Y et al. 2015), weed herbicide resistance (Reddy et al. 2014), water deficiency (Thomson et al. 2012) and nutrient deficiency (Huang et al. 2013a). In image processing and analysis, various methods and algorithms have been developed and applied and with the increase of the data volume and complexity the challenges at data storage, computation and system input and output (I/O) in data management and application will become serious issues. The adoption of the FLTL structure for such data management would help distributed data storage, computational decomposition for parallel processing, and relief of limited system I/O capability, which would result in effective applications driven by processing and analysis of User management and interface Data processing (Remote sensing and others) Crop acreage monitoring Reporting and information dissemination Soil moisture monitoring Crop growth monitoring Crop yield estimation Agro-disaster monitoring Database sub-system Standards and protocols Fig. 4 The structure of the China Agriculture Remote Sensing Monitoring System (CHARMS).