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Addressing the ‘Tower of Babel’ of pesticide regulations: an ontology for supporting pest-control decisions

Published online by Cambridge University Press:  06 November 2019

A. Goldstein Open the ORCID record for A. Goldstein [Opens in a new window] ,
L. Fink ,
O. Raphaeli ,
A. Hetzroni  and
G. Ravid
A. Goldstein*
Affiliation:
Department of Industrial Engineering and Management, Ariel University, Ariel, Israel
L. Fink
Affiliation:
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Be'er-Sheva, Israel
O. Raphaeli
Affiliation:
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Be'er-Sheva, Israel
A. Hetzroni
Affiliation:
Agricultural Research Organization, Volcani Center, Institute of Agricultural Engineering, Rishon LeZion, Israel
G. Ravid
Affiliation:
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Be'er-Sheva, Israel
*
Author for correspondence: A. Goldstein, E-mail: [email protected]
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Abstract

Farmers, who have to decide which pesticide to use against a particular crop-damaging pest, need to take into account country-specific regulations (e.g. permitted levels of pesticide residues), application instructions and financial considerations. The fact that these data are stored in different locations, sometimes using different terminology or different languages, makes it difficult to gather these data and requires that farmers are familiar with the variety of terms used, which consequently hampers the efficiency and effectiveness of the decision process. To overcome these challenges, a Web application for pest control is proposed to facilitate the integration of information coming from different Internet sources and representing different terminologies by using an ontology. The application is based on a pest-control ontology (formal representations of domain knowledge that can be interpreted by computers) that accounts for various pesticide regulations of different countries to which the crop is exported. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision support systems and other intelligent systems, inter alia, in the context of agriculture. The pest-control ontology developed in the current research includes pest-control concepts that have yet to be covered by existing ontologies. It is demonstrated in the specific case of pepper in Israel. The ontology is expressed using Web Ontology Language (OWL) and thus can be shared on the Web and reused by other ontologies and systems. In addition, a comprehensive method for developing and evaluating agricultural ontologies is presented.

Keywords

Agricultural ontologydecision supportontology evaluationOWLpest control
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 157 , Issue 6 , August 2019 , pp. 493 - 503
Copyright
Copyright © Cambridge University Press 2019

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References

AGROVOC Thesaurus. Available at http://aims.fao.org/vest-registry/vocabularies/agrovoc-multilingual-agricultural-thesaurus.Google Scholar
Aqeel-ur, R and Zubair, SA (2011) ONTAgri: scalable service oriented agriculture ontology for precision farming. In International Conference on Agricultural and Biosystems Engineering (ICABE 2011), pp. 1–3.Google Scholar
Beck, HW, Kim, S and Hagan, D (2005) A crop-pest ontology for extension publications. In EFITA/WCCA Joint Congress on IT in Agriculture. Vila Real, Portugal: EFITA/WCCA, pp. 1169–1176.Google Scholar
Beck, H, Morgan, K, Jung, Y, Grunwald, S, Kwon, HY and Wu, J (2010) Ontology-based simulation in agricultural systems modeling. Agricultural Systems 103, 463–477.CrossRefGoogle Scholar
Berners-Lee, T, Hendler, J and Lassila, O (2001) The semantic web. Scientific American 284, 1–3. https://www.scientificamerican.com/article/the-semantic-web/.CrossRefGoogle Scholar
Bose, R and Sugumaran, V (2007) Semantic web technologies for enhancing intelligent DSS environments. In Kulkarni, U, Power, DJ and Sharda, R (eds), Decision Support for Global Enterprises. Boston, MA, USA: Springer, pp. 221–238.CrossRefGoogle Scholar
Brank, J, Grobelnik, M and Mladenić, D (2005) A survey of ontology evaluation techniques. In The Conference on Data Mining and Data Warehouses (SiKDD 2005), October 17, 2005. Ljubljana, Slovenia: Artificial Intelligence Laboratory. Available at http://ailab.ijs.si/dunja/SiKDD2005/Papers/BrankEvaluationSiKDD2005.pdf (Accessed 8 October 2019).Google Scholar
Chandrasekaran, B, Josephson, JR and Benjamins, VR (1999) What are ontologies, and why do we need them? IEEE Intelligent Systems 14, 20–26.CrossRefGoogle Scholar
Chang, C, Xian, G and Li, G (2008) Thesaurus and ontology technology for the improvement of agricultural information retrieval. In Nagatsuka, T and Ninomiya, S (eds), Agricultural Information and IT: Proceedings of IAALD AFITA WCCA 2008, August 24–27, 2008 at Tokyo University of Agriculture. Tokyo, Japan: Tokyo University of Agriculture, pp. 531–536.Google Scholar
Chougule, A, Jha, VK and Mukhopadhyay, D (2016) Adaptive ontology construction method for crop pest management. In Satapathy, S, Bhateja, V and Joshi, A (eds), Proceedings of the International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol. 468. Singapore: Springer, pp. 665–674.CrossRefGoogle Scholar
Corcho, O, Fernández-López, M and Gómez-Pérez, A (2003) Methodologies, tools and languages for building ontologies. Where is their meeting point? Data & Knowledge Engineering 46, 41–64.CrossRefGoogle Scholar
Delir Haghighi, P, Burstein, F, Zaslavsky, A and Arbon, P (2013) Development and evaluation of ontology for intelligent decision support in medical emergency management for mass gatherings. Decision Support Systems 54, 1192–1204.CrossRefGoogle Scholar
De Nicola, A, Missikoff, M and Navigli, R (2009) A software engineering approach to ontology building. Information Systems 34, 258–275.CrossRefGoogle Scholar
European Union (2019) Commission Regulation (EU) 2019/38 of 10 January 2019 amending Annexes II and V to Regulation (EC) No 396/2005 of the European Parliament and of the Council as regards maximum residue levels for iprodione in or on certain products (Text with EEA relevance.). Official Journal of the European Union L9, 94–105.Google Scholar
Fernández-López, M and Gómez-Pérez, A (2002) Overview and analysis of methodologies for building ontologies. Knowledge Engineering Review 17, 129–156.CrossRefGoogle Scholar
Gaire, R, Lefort, L, Compton, M, Falzon, G, Lamb, D and Taylor, K (2013) Demonstration: semantic web enabled smart farm with GSN. In Blomqvist, E and Groza, T (eds), Proceedings of the ISWC 2013 Posters & Demonstrations. Aachen, Germany: Aachen University CEUR-WS.org, pp. 41–44.Google Scholar
Goldstein, A, Fink, L and Ravid, G (2019) A framework for evaluating agricultural ontologies. arXiv Preprint 1906, 10450. Available at https://arxiv.org/abs/1906.10450. (abstract).Google Scholar
Gómez-Pérez, A (1996) Towards a framework to verify knowledge sharing technology. Expert Systems with Applications 11, 519–529.CrossRefGoogle Scholar
Gómez-Pérez, A (2001) Evaluation of ontologies. International Journal of Intelligent Systems 16, 391–409.3.0.CO;2-2>CrossRefGoogle Scholar
Goumopoulos, C, Kameas, AD and Cassells, A (2009) An ontology-driven system architecture for precision agriculture applications. International Journal of Metadata, Semantics and Ontologies 4, 72–84.CrossRefGoogle Scholar
Gruber, TR (1993) A translation approach to portable ontology specifications. Knowledge Acquisition 5, 199–220.CrossRefGoogle Scholar
Gruber, TR (1995) Toward principles for the design of ontologies used for knowledge sharing? International Journal of Human-Computer Studies 43, 907–928.CrossRefGoogle Scholar
Grüninger, M and Fox, MS (1995) Methodology for the design and evaluation of ontologies. In Workshop on Basic Ontological Issues in Knowledge Sharing IJCAI-95, Montreal. Montreal, Canada: IJCAII, AAAI and CSCSI, pp. 1–10.Google Scholar
Janssen, SJC, Porter, CH, Moore, AD, Athanasiadis, IN, Foster, I, Jones, JW and Antle, JM (2017) Towards a new generation of agricultural system data, models and knowledge products: information and communication technology. Agricultural Systems 155, 200–212.CrossRefGoogle ScholarPubMed
Kim, S and Beck, HW (2006) A practical comparison between thesaurus and ontology techniques as a basis for search improvement. Journal of Agricultural & Food Information 7, 23–42.CrossRefGoogle Scholar
Kragt, ME, Pannell, DJ, McVittie, A, Stott, AW, Vosough Ahmadi, B and Wilson, P (2016) Improving interdisciplinary collaboration in bio-economic modelling for agricultural systems. Agricultural Systems 143, 217–224.CrossRefGoogle Scholar
Li, D, Kang, L, Cheng, X, Li, D, Ji, L, Wang, K and Chen, Y (2013) An ontology-based knowledge representation and implement method for crop cultivation standard. Mathematical and Computer Modelling 58, 466–473.CrossRefGoogle Scholar
Liao, J, Li, L and X, L (2015) An integrated, ontology-based agricultural information system. Information Development 31, 150–163.CrossRefGoogle Scholar
Maliappis, MT (2009) Using agricultural ontologies. In Sicilia, MA and Lytras, MD (eds), Metadata and Semantics. Boston, MA, USA: Springer, pp. 493–498.CrossRefGoogle Scholar
Noy, NF and McGuinness, DL (2001) Ontology Development 101: A Guide to Creating your First Ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880. Stanford, CA, USA: Stanford University, pp. 1–25.Google Scholar
Palavitsinis, N and Manouselis, N (2014) Agricultural knowledge organization systems: an analysis of an indicative sample. In Sicilia, MA (ed.), Handbook of Metadata, Semantics and Ontologies. Singapore: World Scientific, pp. 279–296.CrossRefGoogle Scholar
Roussey, C, Soulignac, V, Champomier, JC, Abt, V and Chanet, JP (2010) International Conference on Agricultural Engineering (AgEng 2010). Aubiere, France: Cemagref, pp. 1–10.Google Scholar
Song, G, Wang, M, Ying, X, Yang, R and Zhang, B (2012) Study on precision agriculture knowledge presentation with ontology. AASRI Procedia 3, 732–738.CrossRefGoogle Scholar
Tomic, D, Drenjanac, D, Hoermann, S and Auer, W (2015) Experiences with creating a precision dairy farming ontology (DFO) and a knowledge graph for the data integration platform in agriOpenLink. Agrárinformatika/Journal Of Agricultural Informatics 6, 115–126.Google Scholar
Uschold, M and King, M (1995) Towards a methodology for building ontologies. In Workshop on Basic Ontological Issues in Knowledge Sharing, Held in Conjunction with IJCAI-95. Montreal, Canada: IJCAII, AAAI and CSCSI, pp. 1–13.Google Scholar
US Environmental Protection Agency (2019) Regulation of Pesticide Residues on Food. Washington, DC, USA: US EPA. Available at https://www.epa.gov/pesticide-tolerances (Accessed 16 October 2019).Google Scholar
W3C (2006) Ontology Driven Architecture and Potential Uses of the Semantic Web in Systems and Software Engineering. Cambridge, MA, USA: W3C. Available at http://www.w3.org/2001/sw/BestPractices/SE/ODA/ (Accessed 8 October 2019).Google Scholar
Yu, J, Thom, JA and Tam, A (2005) Evaluating ontology criteria for requirements in a geographic travel domain. In Meersman, R and Tari, Z (eds), On the Move to Meaningful Internet Systems 2005: CoopIS, DOA, and ODBASE. Lecture Notes in Computer Science, vol 3761. Berlin, Germany: Springer, pp. 1517–1534.CrossRefGoogle Scholar
Yu, J, Thom, JA and Tam, A (2007) Ontology evaluation using Wikipedia categories for browsing. In Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management: CIKM'07. New York: ACM, pp. 223–232.CrossRefGoogle Scholar
Zhang, N, Wang, M and Wang, N (2002) Precision agriculture – a worldwide overview. Computers and Electronics in Agriculture 36, 113–132.CrossRefGoogle Scholar