TACO: Bitki Zararlıları, Yabani Otlar ve Hastalıkların Tespiti ve Kontrolü İçin Türkçe Bir Ontoloji

Öz

Akıllı tarımda daha sürdürülebilir bir üretim sağlanırken, kimyasal kullanımının azalması nedeniyle bitki zararlıları, hastalıkları ve yabancı ot kontrolü daha karmaşık hale gelmektedir. Bu nedenle zararlı, hastalık ve yabancı otların erken aşamada tespit edilmesi büyük önem taşımaktadır. Hem çiftçilerin hem de tarımsal mücadele için geliştirilen yapay zeka uygulamalarının bu organizmaları tespit edebilmesi ve tarımsal mücadele yöntemlerini bilmesi önemlidir. Semantik teknolojiler ve ontolojiler, makine tarafından yorumlanabilir bilgiler ve heterojenlik için çözümler sağlar. Bu çalışmada Türkiye'de yaygın olarak görülen bitki zararlıları, hastalıkları ve yabancı otlar hakkında bilgiler içeren Türkçe olarak oluşturulmuş Türk Tarımsal Kontrol Ontolojisi (TACO) sunulmaktadır. Çalışmanın katkıları, bu alanda yapılan ilk Türk ontolojisi olması ve tarımsal mücadele yöntemlerinin ontoloji kapsamında yer almasıdır. Sıkça kullanılan ontoloji değerlendirme metriklerine göre TACO, ağırlıklı olarak derin bir sınıflandırma taksonomisi olarak nitelendirilmiştir. Ayrıca ontolojideki sınıfların eşit olarak dağılım gösteren, yeterli sayıda sınıf örneğine sahip olduğu sonucuna varılmıştır.

Anahtar Kelimeler

• ontoloji
• yapay zeka
• tarımsal kontrol
• bitki zararlıları
• bitki hastalıkları
• yabani otlar

TACO: An Ontology in Turkish for Identifying and Controlling Plant Pests, Weeds and Diseases

Öz

While ensuring a more sustainable production, because of reduced chemical usage it is more complicated to control plant pests, diseases and weeds in smart agriculture. For this reason, it is of great importance to detect pests, diseases and weeds at the earliest stage. It is important that both farmers and the artificial intelligence applications developed for agricultural control should be able to detect these organisms and to know the agricultural control methods. Semantic technologies and ontologies provide machine interpretable information and solutions for heterogeneity. This study presents the Turkish Agricultural Control Ontology (TACO), which is built in Turkish and contains information about plant pests, diseases and weeds common in Turkey. The contributions of the study are that it is the first Turkish ontology built in this field and that the methods of agricultural control are included within the scope of the ontology. According to the commonly used ontology evaluation metrics, TACO is predominantly characterized as a deep classification taxonomy. In addition, it was concluded that the classes in the ontology have an evenly distributed and sufficient number of class individuals.

Anahtar Kelimeler

• ontology
• artificial intelligence
• agricultural control
• plant pests
• plant diseases
• weeds

Kaynakça

1. Tarım ve Orman Bakanlığı, 2019. Akıllı Tarım Platformu.http://www.akillitarim.org/tr/ (Date Of Access: 22.02.2023)
2. Jonquet, C., Toulet, A., Arnaud, E., Aubin, S., Yeumo, E.D., Emonet, V., Graybeal, J., Laporte, M.A., Musen, M.A., Pesce, V., Larmande, P. 2018. AgroPortal: A vocabulary and ontology repository for agronomy: Computers and Electronics in Agriculture, Vol. 144, p. 126-143. DOI:10.1016/j.compag.2017.10.012
3. Jaiswal, P., Avraham, S., Ilic, K., Kellogg, E.A., McCouch, S., Pujar, A., Reiser, L., Rhee, S.Y., Sachs, M.M., Schaeffer, M., Stein, L., Stevens, P., Vincent, L., Ware, D., Zapata, F. 2005. Plant Ontology (PO): a Controlled Vocabulary of Plant Structures and Growth Stages: Comparative and Functional Genomics, Vol. 6(7-8), p. 388-397. DOI: 10.1002/cfg.496
4. Arnaud, E., Cooper, L., Shrestha, R., Menda, N., Nelson, R.T., Matteis, L., Skofic, M., Bastow, R., Jaiswal, P., Mueller, L., McLaren, G. 2012. Towards a reference Plant Trait Ontology for modeling knowledge of plant traits and phenotypes. International Conference on Knowledge Engineering and Ontology Development, 4-7 October, Barcelona, 220–225.
5. European Bioinformatics Institute, 2017. Plant Experimental Conditions Ontology. https://bioportal.bioontology.org/ontologies/PECO (Date Of Access: 22.02.2023)
6. European Bioinformatics Institute, 2020. Plant Stress Ontology. 2020.https://bioportal.bioontology.org/ontologies/PLANTSO (Date Of Access: 22.02.2023)
7. Walls, R.L., Smith, B., Elser, J., Goldfain, A., Stevenson, D.W., Jaiswal, P. 2012. A plant disease extension of the infectious disease ontology. International Conference on Biomedical Ontology, 21-25 July, Graz, 1-5.
8. Aubert, C., Buttigieg, P.L., Laporte, M.A., Devare, M., Arnaud, E. 2017.Agronomy Ontology. http://purl.obolibrary.org/obo/agro.owl (Date Of Access: 22.02.2023)

9. Caracciolo, C., Stellato, A., Morshed, A., Johannsen, G., Rajbahndari, S., Jaques, Y., Keizer J. 2013. The AGROVOC Linked Dataset: Semantic Web Journal, Vol. 4(3), p. 341-348. DOI: 10.5555/2786071.2786087
10. Rodriguez-Garcia, M.A., Garcia-Sanchez, F. 2020. CropPestO: An Ontology Model for Identifying and Managing Plant Pests and Diseases: Communications in Computer and Information Science, Vol. 1309, p. 18-29. DOI: 10.1007/978-3-030-62015-8_2
11. Onkov, K. 2020. Ontology of Crop Pest Control. 3rd International Conference on Information Science and Systems, 19–22 March, Cambridge, 8–12.
12. Iglesias, A.R., Aranguren, M. E., González, A.R., Wilkinson, M.D. 2013. Plant Pathogen Interactions Ontology (PPIO). International WorkConference on Bioinformatics and Biomedical Engineering, 18-20 March, Gradana, 695-702.
13. Lacasta, J., Lopez-Pellicer, F.J., Espejo-García, B., Nogueras-Iso, J., Zarazaga-Soria, F.J. 2018. Agricultural recommendation system for crop protection: Computers and Electronics in Agriculture, Vol. 152, p. 82-89. DOI: 10.1016/j.compag.2018.06.049246 DEU FMD 26(77) (2024) 242-247
14. Medici, M., Dooley, D., Canavari, M. 2022. PestOn: An Ontology to Make Pesticides Information Easily Accessible and Interoperable: Sustainability, Vol. 14, p. 66-73. DOI: 10.3390/su14116673
15. TAGEM, 2018. Zirai Mücadele Teknik Talimatları Kitapları Cilt 1-Cilt 6.T.C. Tarım Orman ve Hayvancılık Bakanlığı, Ankara, 1841 p.
16. Noy, N., Fergerson, R., Musen, M. 2000. The knowledge model of Protégé2000: combining interoperability and flexibility, 12th International Conference on Knowledge Engineering and Knowledge Management, 26 October, French Riviera, 17-32.
17. Bizer, C., Vidal, M.E., Skaf-Molli, H. 2018. Linked Open Data. p 2096-2101.Liu, L., Özsu, M.T., ed. 2018. Encyclopedia of Database Systems, Springer Nature, Berlin, 4866 p.
18. Welty, C., McGuinness, D.L., Smith, M.K. 2004. OWL Web ontology language guide. https://www.w3.org/TR/owl-guide/ (Date Of Access:22.02.2023)
19. Tartır, S., Arpınar, I.B., Moore, M., Sheth, A.P., Aleman-Meza, B. 2005. OntoQA: Metric-based ontology quality analysis. IEEE Workshop on Knowledge Acquisition from Distributed, Autonomous, Semantically Heterogeneous Data and Knowledge Sources, 27 November, New Orleans, 45-53.
20. Ebietomer, E.P., Ekuobase, G.O. 2013. Evaluation of Ontology for Nigerian Case Laws: Computing, Information Systems, Development Informatics & Allied Research Journal, Vol. 4(3), p. 1-6.