Special Issue "Semantic Technologies Applied to Agriculture"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 August 2020).

Special Issue Editors

Dr. Francisco García-Sánchez
E-Mail Website
Guest Editor
Departamento de Informática y Sistemas, Facultad de Informática, Universidad de Murcia, Campus de Espinardo, Espinardo 30100, Murcia, Spain
Interests: semantic web; knowledge engineering; ontologies; linked data; social semantic web; distributed systems; service oriented architectures; cloud computing; artificial intelligence; natural language processing; intelligent agents and multiagent systems
Dr. Miguel Ángel Rodríguez-García
E-Mail Website
Guest Editor
Departamento de Ciencias de la Computación, Arquitectura de Computadores, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Universidad Rey Juan Carlos, Tulipán, s/n, Móstoles 28933, Madrid, Spain
Interests: semantic web; knowledge engineering; ontologies; linked data; artificial intelligence; natural language processing; intelligent agents and multiagent systems; natural language processing; semantic web technologies; computational biology; biomedical informatics; optimization; heuristics; metaheuristics

Special Issue Information

Dear Colleagues,

Today, the use of new technologies in agriculture is almost pervasive across the world. Smart farming refers to using high-tech farming techniques and technologies to improve production output while minimizing cost and preserving resources. The main applications of ICT in agriculture (also known as ‘e-agriculture’) include the use of GPS and Geographic Information Systems (GIS) for precision farming, smartphone apps for e-learning and crop management, RFID for product tracking, knowledge management systems with information and best practices, etc. New technologies promote sustainable agricultural development and food security by improving the use of information, communication, and associated technologies in the sector.

Large amounts of data are being generated and made available to third parties in various formats from heterogeneous, disparate data sources. Ontologies and semantic technologies are a useful means to integrate and harmonize data from different sources and facilitate inferring and reasoning over a shared conceptualization. A number of different vocabularies and ontologies for the agronomic community have been defined for different purposes and most of them are being hosted in the AgroPortal ontology repository (http://agroportal.lirmm.fr/).

This Special Issue is devoted to papers concerned with the application of semantic technologies in the agronomy domain. We seek novel contributions and successful use cases in which the benefits of using ontologies on this field are made clear.

Prof. Dr. Francisco García-Sánchez
Dr. Miguel Ángel Rodríguez-García
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Semantic web
  • Ontology
  • Knowledge management
  • Data integration
  • Semantic services
  • Agriculture
  • Agronomy
  • Smart farming
  • E-agriculture

Published Papers (5 papers)

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Research

Open AccessArticle
Linked Data Platform for Solanaceae Species
Appl. Sci. 2020, 10(19), 6813; https://doi.org/10.3390/app10196813 - 28 Sep 2020
Viewed by 901
Abstract
Genetics research is increasingly focusing on mining fully sequenced genomes and their annotations to identify the causal genes associated with traits (phenotypes) of interest. However, a complex trait is typically associated with multiple quantitative trait loci (QTLs), each comprising many genes, that can [...] Read more.
Genetics research is increasingly focusing on mining fully sequenced genomes and their annotations to identify the causal genes associated with traits (phenotypes) of interest. However, a complex trait is typically associated with multiple quantitative trait loci (QTLs), each comprising many genes, that can positively or negatively affect the trait of interest. To help breeders in ranking candidate genes, we developed an analytical platform called pbg-ld that provides semantically integrated geno- and phenotypic data on Solanaceae species. This platform combines both unstructured data from scientific literature and structured data from publicly available biological databases using the Linked Data approach. In particular, QTLs were extracted from tables of full-text articles from the Europe PubMed Central (PMC) repository using QTLTableMiner++ (QTM), while the genomic annotations were obtained from the Sol Genomics Network (SGN), UniProt and Ensembl Plants databases. These datasets were transformed into Linked Data graphs, which include cross-references to many other relevant databases such as Gramene, Plant Reactome, InterPro and KEGG Orthology (KO). Users can query and analyze the integrated data through a web interface or programmatically via the SPARQL and RESTful services (APIs). We illustrate the usability of pbg-ld by querying genome annotations, by comparing genome graphs, and by two biological use cases in Jupyter Notebooks. In the first use case, we performed a comparative genomics study using pbg-ld to compare the difference in the genetic mechanism underlying tomato fruit shape and potato tuber shape. In the second use case, we developed a seamlessly integrated workflow that uses genomic data from pbg-ld knowledge graphs and prioritization pipelines to predict candidate genes within QTL regions for metabolic traits of tomato. Full article
(This article belongs to the Special Issue Semantic Technologies Applied to Agriculture)
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Open AccessArticle
Semantic and Syntactic Interoperability for Agricultural Open-Data Platforms in the Context of IoT Using Crop-Specific Trait Ontologies
Appl. Sci. 2020, 10(13), 4460; https://doi.org/10.3390/app10134460 - 28 Jun 2020
Cited by 2 | Viewed by 688
Abstract
In recent years, Internet-of-Things (IoT)-based applications have been used in various domains such as health, industry and agriculture. Considerable amounts of data in diverse formats are collected from wireless sensor networks (WSNs) integrated into IoT devices. Semantic interoperability of data gathered from IoT [...] Read more.
In recent years, Internet-of-Things (IoT)-based applications have been used in various domains such as health, industry and agriculture. Considerable amounts of data in diverse formats are collected from wireless sensor networks (WSNs) integrated into IoT devices. Semantic interoperability of data gathered from IoT devices is generally being carried out using existing sensor ontologies. However, crop-specific trait ontologies—which include site-specific parameters concerning hazelnut as a particular agricultural product—can be used to make links between domain-specific variables and sensor measurement values as well. This research seeks to address how to use crop-specific trait ontologies for linking site-specific parameters to sensor measurement values. A data-integration approach for semantic and syntactic interoperability is proposed to achieve this objective. An open-data platform is developed and its usability is evaluated to justify the viability of the proposed approach. Furthermore, this research shows how to use web services and APIs to carry out the syntactic interoperability of sensor data in agriculture domain. Full article
(This article belongs to the Special Issue Semantic Technologies Applied to Agriculture)
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Open AccessArticle
Development Experience of a Context-Aware System for Smart Irrigation Using CASO and IRRIG Ontologies
Appl. Sci. 2020, 10(5), 1803; https://doi.org/10.3390/app10051803 - 05 Mar 2020
Cited by 4 | Viewed by 1109
Abstract
The rapid development of information and communication technologies and wireless sensor networks has transformed agriculture practices. New tools and methods are used to support farmers in their activities. This paper presents a context-aware system that automates irrigation decisions based on sensor measurements. Automatic [...] Read more.
The rapid development of information and communication technologies and wireless sensor networks has transformed agriculture practices. New tools and methods are used to support farmers in their activities. This paper presents a context-aware system that automates irrigation decisions based on sensor measurements. Automatic irrigation overcomes the water shortage problem, and automatic sensor measurements reduce the observational work of farmers. This paper focuses on a method for developing context-aware systems using ontologies. Ontologies are used to solve heterogeneity issues in sensor measurements. Their main goal is to propose a shared data schema that precisely describes measurements to ease their interpretations. These descriptions are reusable by any machine and understandable by humans. The context-aware system also contains a decision support system based on a rules inference engine. We propose two new ontologies: The Context-Aware System Ontology addresses the development of the context-aware system in general. The Irrigation ontology automates a manual irrigation method named IRRINOV®. These ontologies reuse well-known ontologies such as the Semantic Sensor Network (SSN) and Smart Appliance REFerence (SAREF). The decision support system uses a set of rules with ontologies to infer daily irrigation decisions for farmers. This project uses real experimental data to evaluate the implementation of the decision support system. Full article
(This article belongs to the Special Issue Semantic Technologies Applied to Agriculture)
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Open AccessArticle
AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops
Appl. Sci. 2020, 10(3), 1040; https://doi.org/10.3390/app10031040 - 04 Feb 2020
Cited by 5 | Viewed by 776
Abstract
In the agricultural context, there is a great diversity of insects and diseases that affect crops. Moreover, the amount of data available on data sources such as the Web regarding these topics increase every day. This fact can represent a problem when farmers [...] Read more.
In the agricultural context, there is a great diversity of insects and diseases that affect crops. Moreover, the amount of data available on data sources such as the Web regarding these topics increase every day. This fact can represent a problem when farmers want to make decisions based on this large and dynamic amount of information. This work presents AgriEnt, a knowledge-based Web platform focused on supporting farmers in the decision-making process concerning crop insect pest diagnosis and management. AgriEnt relies on a layered functional architecture comprising four layers: the data layer, the semantic layer, the web services layer, and the presentation layer. This platform takes advantage of ontologies to formally and explicitly describe agricultural entomology experts’ knowledge and to perform insect pest diagnosis. Finally, to validate the AgriEnt platform, we describe a case study on diagnosing the insect pest affecting a crop. The results show that AgriEnt, through the use of the ontology, has proven to produce similar answers as the professional advice given by the entomology experts involved in the evaluation process. Therefore, this platform can guide farmers to make better decisions concerning crop insect pest diagnosis and management. Full article
(This article belongs to the Special Issue Semantic Technologies Applied to Agriculture)
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Open AccessArticle
Semantic Publication of Agricultural Scientific Literature Using Property Graphs
Appl. Sci. 2020, 10(3), 861; https://doi.org/10.3390/app10030861 - 26 Jan 2020
Cited by 3 | Viewed by 1119
Abstract
During the last decades, there have been significant changes in science that have provoked a big increase in the number of articles published every year. This increment implies a new difficulty for scientists, who have to do an extra effort for selecting literature [...] Read more.
During the last decades, there have been significant changes in science that have provoked a big increase in the number of articles published every year. This increment implies a new difficulty for scientists, who have to do an extra effort for selecting literature relevant for their activity. In this work, we present a pipeline for the generation of scientific literature knowledge graphs in the agriculture domain. The pipeline combines Semantic Web and natural language processing technologies, which make data understandable by computer agents, empowering the development of final user applications for literature searches. This workflow consists of (1) RDF generation, including metadata and contents; (2) semantic annotation of the content; and (3) property graph population by adding domain knowledge from ontologies, in addition to the previously generated RDF data describing the articles. This pipeline was applied to a set of 127 agriculture articles, generating a knowledge graph implemented in Neo4j, publicly available on Docker. The potential of our model is illustrated through a series of queries and use cases, which not only include queries about authors or references but also deal with article similarity or clustering based on semantic annotation, which is facilitated by the inclusion of domain ontologies in the graph. Full article
(This article belongs to the Special Issue Semantic Technologies Applied to Agriculture)
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