Towards the Integration of Agricultural Data from Heterogeneous Sources: Perspectives for the French Agricultural Context Using Semantic Technologies

Jiang, Shufan; Angarita, Rafael; Chiky, Raja; Cormier, Stéphane; Rousseaux, Francis

doi:10.1007/978-3-030-49165-9_8

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…To tackle the lack of semantic interoperability in data processing, it is necessary to describe all data elements by developing vocabularies and schema to ensure that all communicating agricultural parties understand the data in the same way. The solutions for improving semantic interoperability in smart farming are standardization, metadata, and connecting each data variable to a common language, in the form of taxonomies and ontologies [56]. Semantic interoperability issues in smart farming data processes can be solved by using standardized languages such as agroXML [57] which is an XML dialect for describing farm production processes as well as the real-world objects required in conducting these processes.…”

Section: Interoperabilitymentioning

confidence: 99%

A Platform Approach to Smart Farm Information Processing

et al. 2022

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With the rapid growth of population and the increasing demand for food worldwide, improving productivity in farming procedures is essential. Smart farming is a concept that emphasizes the use of modern technologies such as the Internet of Things (IoT) and artificial intelligence (AI) to enhance productivity in farming practices. In a smart farming scenario, large amounts of data are collected from diverse sources such as wireless sensor networks, network-connected weather stations, monitoring cameras, and smartphones. These data are valuable resources to be used in data-driven services and decision support systems (DSS) in farming applications. However, one of the major challenges with these large amounts of agriculture data is their immense diversity in terms of format and meaning. Moreover, the different services and technologies in a smart farming ecosystem have limited capability to work together due to the lack of standardized practices for data and system integration. These issues create a significant challenge in cooperative service provision, data and technology integration, and data-sharing practices. To address these issues, in this paper, we propose the platform approach, a design approach intended to guide building effective, reliable, and robust smart farming systems. The proposed platform approach considers six requirements for seamless integration, processing, and use of farm data. These requirements in a smart farming platform include interoperability, reliability, scalability, real-time data processing, end-to-end security and privacy, and standardized regulations and policies. A smart farming platform that considers these requirements leads to increased productivity, profitability, and performance of connected smart farms. In this paper, we aim at introducing the platform approach concept for smart farming and reviewing the requirements for this approach.

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Section: Interoperabilitymentioning

confidence: 99%

A Platform Approach to Smart Farm Information Processing

et al. 2022

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“…The entities and the relations among them are the essential elements of formal knowledge graph construction. We can use the knowledge graph to index and integrate information from heterogeneous documents [3]. Given a text, we split it into a sequence of tokens S = (w 1 , w 2 , ..., w n ), the goal of NER is then to identify whether a subsequence S ′ = (w k , ..., w l ), (1 ≤ k ≤ l ≤ n) is an entity.…”

Section: Introductionmentioning

confidence: 99%

Named Entity Recognition for Monitoring Plant Health Threats in Tweets: a ChouBERT Approach

Jiang

Angarita

Cormier

et al. 2022

2022 6th International Conference on Universal Village (UV)

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Copyright

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“…This is partially due to the difficulty associated with the integration of heterogeneous data coming from disparate sources. Ontologies and other related semantic technologies have proven effective for data integration in multiple domains [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Based System for Crop Pests and Diseases Recognition

2021

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With the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few years the worldwide dispersion of virulent plant pests and diseases has caused significant decreases in the yield and quality of crops, in particular fruit, cereal and vegetables. Climate change and the intensification of global trade flows further accentuate the issue. Integrated Pest Management (IPM) is an approach to pest control that aims at maintaining pest insects at tolerable levels, keeping pest populations below an economic injury level. Under these circumstances, the early identification of pests and diseases becomes crucial. In this work, we present the first step towards a fully fledged, semantically enhanced decision support system for IPM. The ultimate goal is to build a complete agricultural knowledge base by gathering data from multiple, heterogeneous sources and to develop a system to assist farmers in decision making concerning the control of pests and diseases. The pest classifier framework has been evaluated in a simulated environment, obtaining an aggregated accuracy of 98.8%.

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Towards the Integration of Agricultural Data from Heterogeneous Sources: Perspectives for the French Agricultural Context Using Semantic Technologies

Cited by 12 publications

References 13 publications

A Platform Approach to Smart Farm Information Processing

A Platform Approach to Smart Farm Information Processing

Named Entity Recognition for Monitoring Plant Health Threats in Tweets: a ChouBERT Approach

Knowledge-Based System for Crop Pests and Diseases Recognition

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