Tool, toolmaker, and scientist: case study experiences using GIS in interdisciplinary research

Ricker, Britta; Rickles, Patrick; Fagg, Gretchen; Haklay, Muki

doi:10.1080/15230406.2020.1748113

Cited by 6 publications

(2 citation statements)

References 64 publications

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“…Novák et al explained how to deploy IoT technologies within agriculture operations with a focus on fuzzy logic in [48]. There are many studies on using IoT in different applications such as in [49]- [51]. But at the moment, there is no known research that provides a solution on how to bring research into reality and get the farmers to benefit from researches [49]- [53].…”

Section: Fig1 the Missing Links In National Collaboration In Agriculmentioning

confidence: 99%

Systems Science: Digital Transformation andDeveloping Business Model toward Smart Farms’platform

Ibrahim

Elramly

Hassan

2021

International Journal of Circuits, Systems and Signal Processing

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This paper describes efforts by National Authority for Remote Sensing and Space Sciences (NARSS) to help the Egyptian government to manage and monitor the national projects. We successfully developed a geospatial data sharing portal (NARSSGeoPortal) as part of the government need to build national Decision Support System (DSS). We were able to solve the software development issues as well as the satellite imagery sourcing issues, but the main challenge remains around how to collect complete and correct data from the public about their private businesses nationwide. The most challenging is how to engage the public and encourage the business owners who are the main sources of data to provide the government Geoportal with data about their businesses. It is also challenging to engage the scientists and experts from government research centers into the data sharing Geoportal. Furthermore, it is a challenge to integrate the government research centers with the public businesses’ daily operation. The data sharing Geoportal is built for all national projects and government authorities, however, in this paper we focus on the Agriculture authorities and farming businesses where the challenge is how to collect correct and complete data per acre about the seeds, fertilizers, water, pest control and all other farm related data that the satellite imagery does not provide. The goal is to integrate the farms into unified national monitoring, and control system while developing advanced smart farms with the use of Internet of Things (IoT). The proposed collaboration agriculture platform fills the gap between two groups. The first group includes the government authorities, financial institutions, and research centers. The second group includes farmers, supply chain, and agriculture engineers. The platform show how employment can be generated by transforming the national ecosystem. The paper also fills a major gap in industry as well as in academia by providing the first Bluetooth Low Energy computer aided design tool that will facilitate testing, designing, deploying, managing and debugging of real Bluetooth Low Energy networks.

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Section: Fig1 the Missing Links In National Collaboration In Agriculmentioning

confidence: 99%

Systems Science: Digital Transformation andDeveloping Business Model toward Smart Farms’platform

Ibrahim

Elramly

Hassan

2021

International Journal of Circuits, Systems and Signal Processing

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“…Therefore, the idea of having georeferenced information is by no means new. While early use of spatial data was driven by environmental disciplines in which space itself is fundamental such as geography, soil science, and climatology, the value of spatial data is more and more recognized in interdisciplinary research [337]. In the social sciences, the regional sciences are probably the most prominent advocates and users of spatial economic data.…”

Section: Agri-environmental Data Integrationmentioning

confidence: 99%

Perspectives on mutual dependence in plant health

Schneider

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List of Figures 5.15 Presence and random background points used for training (cumulative over time) and validating in the sixth fold of the temporal cross-validation. . . . . . . . . . . . . 154 5.16 Overview of presences and random background points falling into the different continental spatial blocks. . . . . . 155 5.17 Cross validation performance, in terms of area under the ROC, for all approaches for cross validation and background point generation.

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