Integrated open geospatial web service enabled cyber-physical information infrastructure for precision agriculture monitoring

Chen, Nengcheng; Zhang, Xiang; Wang, Chao

doi:10.1016/j.compag.2014.12.009

Cited by 81 publications

(20 citation statements)

References 22 publications

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“…In another example, a web service has been designed for exploiting the availability of global navigation satellite system reflectometry (GNSS-R) signals for assessing soil moisture [29]. Agricultural monitoring is also facilitated by open geospatial web services that integrate heterogeneous information from multiple wireless and wired sensor networks [30].…”

Section: Precision Agriculture and Spatial Decision Support Toolsmentioning

confidence: 99%

Adoption of Web-Based Spatial Tools by Agricultural Producers: Conversations with Seven Northeastern Ontario Farmers Using the GeoVisage Decision Support System

et al. 2017

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This paper reports on the findings of a multi-site qualitative case study research project designed to document the utility and perceived usefulness of weather station and imagery data associated with the online resource GeoVisage among northeastern Ontario farmers. Interviews were conducted onsite at five participating farms (three dairy, one cash crop, and one public access fruit/vegetable) in 2014-2016, and these conversations were transcribed and returned to participants for member checking. Interview data was then entered into Atlas.ti software for the purpose of qualitative thematic analysis. Fifteen codes emerged from the data and findings center around three overarching themes: common uses of weather station data (e.g., air/soil temperature, rainfall); the use of GeoVisage Imagery data/tools (e.g., acreage calculations, remotely sensed imagery); and future recommendations for the online resource (e.g., communication, secure crop imagery, mobile access). Overall, weather station data and tools freely accessible through the GeoVisage site were viewed as representing a timely, positive, and important addition to contemporary agricultural decision-making in northeastern Ontario farming.

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Section: Precision Agriculture and Spatial Decision Support Toolsmentioning

confidence: 99%

Adoption of Web-Based Spatial Tools by Agricultural Producers: Conversations with Seven Northeastern Ontario Farmers Using the GeoVisage Decision Support System

et al. 2017

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“…Intelligence-enabling information technologies that can potentially empower FEW systems analysis, planning, design, management, and operation include (1) perception using sensing and data acquisition and management technologies (e.g., internet of things); (2) reasoning and learning involving mathematical, statistical, logical, and heuristic methodologies, handling of incomplete and uncertain information, and data mining (e.g., big data); (3) communication considering the contents, sources and recipients, and delivery platforms including wired, wireless, local area networks, wide area networks, the internet, and mobile technologies and devices (e.g., the nerves of information systems); (4) task planning and execution that involves control logic, planning of physical tasks, intelligent machines, robotics, and flexible automation work cells (i.e., physical capacity); and (5) systems integration to provide computational resources and capabilities of systems informatics, modeling, analysis, decision support, design and specifications, logistics and model-based control, concurrent science, engineering, and technology, and implementation (i.e., cyber-physical systems) (Chen et al 2015).…”

Section: Food-energy-water Systemsmentioning

confidence: 99%

Engineering solutions for food-energy-water systems: it is more than engineering

et al. 2016

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physical framework comprised of systems informatics, information analysis methods and tools, and systems analytics and decision support could provide a viable approach for addressing FEW system challenges. A fundamental requirement for implementing the framework is data. Needed data are often difficult to obtain; for example, while much agricultural production system data are collected, the data are not generally available. A priority for addressing FEW system challenges must be development of mechanisms for widespread curation and sharing of data; a few such efforts are underway. Implementing the framework also requires many collaborations. Creating new collaborations among multiple disciplines and organizations to implement the framework could be aided by convergence thinking, which engages approaches to problem solving that transcend disciplines and integrates knowledge from the physical, biological, social, and mathematical sciences and engineering to form comprehensive and integrated thinking at the interfaces of areas. A variety of organizations, private and public, can help in facilitating collaboration and partnerships among the disciplines. Government agencies, industry, academia, and professional societies can all play significant roles in furthering collaboration to address challenges in integrated FEW systems using a systems approach.

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“…Consequently, total miss hits δ o pNq within which new data is not successfully acquired can be calculated by Equation (1), the total request number δ d pNq sent to SOS can be computed by Equation (2), the number of data delayed ε o pNq can be a summation equation defined by Equation (3), and the total number of SOS data ε d pNq is N as shown in Equation (4). V is the number of successful requests.…”

Section: Phrases Meaningmentioning

confidence: 99%

“…The sensors which observe soil moisture are deployed outdoors for modern agriculture [4], usually charged by solar energy. At daytime, if it is sunny or daylight is very adequate, the power absorbed by the solar battery is sufficient for the sensors to work.…”

Section: Observations Of Soil Moisturementioning

confidence: 99%

“…A lot of sensors are deployed all over the world, continuously monitoring features and geo-objects on the Earth's surface [1,2], such as mining industry [3], agriculture [4,5], metropolis [6], and atmosphere [7], producing geographic data unceasingly. Given the development of service-oriented science [8], data can be accessed by anyone, from anywhere and in any form [9].…”

Section: Introductionmentioning

confidence: 99%

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Pull-Based Modeling and Algorithms for Real-Time Provision of High-Frequency Sensor Data from Sensor Observation Services

Fan

et al. 2016

IJGI

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Abstract:The widely used pull-based method for high-frequency sensor data acquisition from Sensor Observation Services (SOS) is not efficient in real-time applications; therefore, further attention must be paid to real-time mechanisms in the provision process if sensor webs are to achieve their full potential. To address this problem, we created a data provision problem model, and compare the recursive algorithm Kalman Filter (KF) and our two proposed self-adaptive linear algorithms Harvestor Additive Increase and Multiplicative Decrease (H-AIMD) and Harvestor Multiplicative Increase and Additive Decrease (H-MIAD) with the commonly used Static Policy, which requests data with an unchanged time interval. We also developed a comprehensive performance evaluation method that considers the real-time capacity and resource waste to compare the performance of the four data provision algorithms. Experiments with real sensor data show that the Static Policy needs accurate priori parameters, Kalman Filter is most suitable for the data provision of sensors with long-term stable time intervals, and H-AIMD is the steadiest with better efficiency and less delayed number of data while with a higher resource waste than the others for data streams with much fluctuations in time intervals. The proposed model and algorithms are useful as a basic reference for real-time applications by pull-based stream data acquisition.

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Integrated open geospatial web service enabled cyber-physical information infrastructure for precision agriculture monitoring

Cited by 81 publications

References 22 publications

Adoption of Web-Based Spatial Tools by Agricultural Producers: Conversations with Seven Northeastern Ontario Farmers Using the GeoVisage Decision Support System

Adoption of Web-Based Spatial Tools by Agricultural Producers: Conversations with Seven Northeastern Ontario Farmers Using the GeoVisage Decision Support System

Engineering solutions for food-energy-water systems: it is more than engineering

Pull-Based Modeling and Algorithms for Real-Time Provision of High-Frequency Sensor Data from Sensor Observation Services

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