Building an interoperable space for smart agriculture

Roussaki, Ioanna; Doolin, Kevin; Skarmeta, Antonio F.; Routis, George; López-Morales, Juan Antonio; Claffey, Ethel; Mora, Manuel; Martı́nez, Juan Antonio

doi:10.1016/j.dcan.2022.02.004

Cited by 42 publications

(21 citation statements)

References 22 publications

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“…Data includes the parameters measured from the ambient, crop and soil using the sensors (5,21) . The data goes through filtering routines (18) , Statistical, Artificial Intelligence (AI) and Machine Learning (ML) techniques (5,19,22,23) for getting only the right data and correct decisions. Finally, Actuation refers to the physical execution of an action commanded by the decision system.…”

Section: Data-driven Smart Agriculture Platformmentioning

confidence: 99%

Spatiotemporal Database Schema for Data Driven Applications in Smart Agriculture

Rao¹

2023

IJST

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Background: Data driven sustainable agriculture involves the collection, storage, processing, and analysis of enormous spatiotemporal data related to crop production processes, systems, infrastructure, and environment. Literature survey on smart agriculture research projects indicates that there is a need for improving handling of spatiotemporal data. There are gaps in handling spatial and temporal variability of parameters, division of crop field into management zones to reduce the variability for optimizing application of inputs such as fertilizers, water or pesticides. These gaps are to be addressed at design level of spatiotemporal database for smart agriculture. Objective: To engineer a spatiotemporal database schema that can be used for data driven sustainable agriculture. Methods: The methodology involves spatiotemporal data representation and modeling, Object oriented analysis and design of the database and Verification of the database using life cycle model and algorithmic steps. Findings: The resulting database is capable to store spatial and temporal variability of soil, plant and water parameters as well as handle spatial split, spatial merge, geometry and location changes of spatiotemporal objects. Novelty: Novel Use cases in smart agriculture along with spatiotemporal attributes are identified so that efficient applications can be realized. Adaption of the spatiotemporal database schema for Smart Irrigation System and its implementation methodology are presented.

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Section: Data-driven Smart Agriculture Platformmentioning

confidence: 99%

Spatiotemporal Database Schema for Data Driven Applications in Smart Agriculture

Rao¹

2023

IJST

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“…This is evidenced by a range of large-scale European industry-research partnership projects addressing the technical challenges of building systems that are functional in diverse contexts and at scale (e.g. SmartAgriHubs (Bacco et al 2019), EIP-AGRI (Kempenaar 2014), DEMETER (Roussaki et al 2022), ATLAS (Smith et al 2021)).…”

Section: Shifting To Outcome-based Payments and Implications For Moni...mentioning

confidence: 99%

“…Sensors measuring trace gas elements, water flow rates, and soil moisture and temperature probes are all available, if less widely used in commercial contexts, to provide high temporal resolution monitoring data. IoT sensors ranging from leaf clips (Gupta et al 2020) to multispectral cameras for monitoring crop development and conditions are appearing alongside more established soil condition monitoring sensors (Roussaki et al 2022). For both established and newer sensor types, the ability to send data to a cloud platform from a remote location has generated renewed interest (Elijah et al 2018;Colizzi et al 2020;Fastellini and Schillaci 2020).…”

Section: Installed Sensors Iot (Internet Of Things) and Networked Datamentioning

confidence: 99%

Remote Sensing Data to Support Integrated Decision Making in Cultural and Natural Heritage Management. Impasses and opportunities for collaboration in agricultural areas

Opitz¹,

Baldwin²,

Smedt³

et al. 2023

Internet Archaeol.

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Remote and near surface sensing data are widely used in archaeology and heritage management for feature discovery, change detection and monitoring, as an input to predictive modelling, and in the planning process. While global and regional datasets are widely used for some purposes, data are regularly acquired specifically for archaeological projects because of the very high spatial resolution required for feature detection and assessments of archaeological significance and the need for data on subsurface features. The sensing data collected for archaeology cover limited areas and only a few types of sensors, known to produce data efficiently, are regularly employed. Precision agriculture is beginning to produce large quantities of varied sensing data across extensive landscape areas. This situation creates an opportunity to adapt and reuse precision agricultural data for archaeology and heritage work, extending covering and enhancing our understanding of archaeology in contemporary agricultural landscapes. Equally, there is potential for coordinated data collection, collecting data once for multiple applications, and to add value through analyses which bring together perspectives from multiple related domains to model long-term processes in anthropogenic soil systems. This article provides a high-level overview of policy and technological developments which create the potential for sensing data reuse, coordinated data collection, and collaborative analyses across archaeological, agricultural, and agri-environmental applications while underscoring the structural barriers which, at present, constrain this potential. It highlights examples where the development of interoperable data and workflows can promote tighter integration of archaeology and cultural heritage management with sustainable agricultural land management and support integrated decision making.

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“…In this regard, there are numerous publications related to data-capture mechanisms in the field and the use of platforms for their management and visualization [10][11][12][13][14][15]. In a complementary way, important efforts are being made to create common data spaces in the agricultural field [16][17][18][19][20][21][22], which try to overcome the existing barriers regarding the global management of data. The main obstacles found are the following: the complexity of data management [23], the lack of interoperability [16,17,22], the insufficiency of storage units and processing platforms [24,25], as well as the scarcity of reference architectures [23,[26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Methodology for the Automatic Inventory of Olive Groves at the Plot and Polygon Level

2022

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The aim of this study was to develop and validate a methodology to carry out olive grove inventories based on open data sources and automatic photogrammetric and satellite image analysis techniques. To do so, tools and protocols have been developed that have made it possible to automate the capture of images of different characteristics and origins, enable the use of open data sources, as well as integrating and metadating them. They can then be used for the development and validation of algorithms that allow for improving the characterization of olive grove surfaces at the plot and cadastral polygon scales. With the proposed system, an inventory of the Andalusian olive grove has been automatically carried out at the level of cadastral polygons and provinces, which has accounted for a total of 1,519,438 hectares and 171,980,593 olive trees. These data have been contrasted with various official statistical sources, thus ensuring their reliability and even identifying some inconsistencies or errors of some sources. Likewise, the capacity of the Sentinel 2 satellite images to estimate the FCC at the cadastral polygon, parcel and 10 × 10 m pixel level has been demonstrated and quantified, as well as the opportunity to carry out inventories with temporal resolutions of approximately up to 5 days.

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Building an interoperable space for smart agriculture

Cited by 42 publications

References 22 publications

Spatiotemporal Database Schema for Data Driven Applications in Smart Agriculture

Spatiotemporal Database Schema for Data Driven Applications in Smart Agriculture

Remote Sensing Data to Support Integrated Decision Making in Cultural and Natural Heritage Management. Impasses and opportunities for collaboration in agricultural areas

Methodology for the Automatic Inventory of Olive Groves at the Plot and Polygon Level

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