Skip to main content

Pest Recognition Using Natural Language Processing

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1124))

Abstract

Agriculture and pest control are fundamental for ensuring worldwide food provisioning. ICT-based systems have proven to be useful for various tasks in the agronomy domain. In particular, several pest recognition tools have been developed that assist in the early identification of plant pests and diseases. However, in most cases expensive devices (e.g., high-resolution cameras) are necessary in association with such tools. In general, smallholders do not have access to those sophisticated devices and so cannot benefit from those tools. In this work, we present a Web-based application that makes use of natural language processing technologies to help (inexperienced) farm workers and managers in recognizing the pests or diseases affecting their crops. End users should submit a text describing the visible symptoms in the plant, and the application returns a sorted list of the most likely causes of the described problem along with the recommended treatments. The prototypical implementation is restricted to the known pathogens infecting almond trees, a crop very rooted in the Spanish agriculture. Early tests have shown promising results.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://www.mapa.gob.es/es/agricultura/temas/sanidad-vegetal/productos-fitosanitarios/guias-gestion-plagas/default.aspx.

References

  1. Current World Population. https://www.worldometers.info/world-population/. Accessed 04 Aug 2019

  2. How to Use Fruits and Vegetables to Help Manage Your Weight. https://www.cdc.gov/healthyweight/healthy_eating/fruits_vegetables.html. Accessed 04 Aug 2019

  3. Healthy Eating Plate & Healthy Eating Pyramid. https://www.hsph.harvard.edu/nutritionsource/healthy-eating-plate/. Accessed 04 Aug 2019

  4. Loizou, E., Karelakis, C., Galanopoulos, K., Mattas, K.: The role of agriculture as a development tool for a regional economy. Agric. Syst. 173, 482–490 (2019). https://doi.org/10.1016/J.AGSY.2019.04.002

    Article  Google Scholar 

  5. Woodard, J., et al.: ICT in Agriculture (Updated Edition): Connecting Smallholders to Knowledge, Networks, and Institutions. The World Bank (2017). https://doi.org/10.1596/978-1-4648-1002-2

  6. Velásquez, A.C., Castroverde, C.D.M., He, S.Y.: Plant-pathogen warfare under changing climate conditions. Curr. Biol. 28, R619–R634 (2018). https://doi.org/10.1016/j.cub.2018.03.054

    Article  Google Scholar 

  7. Pan, L., et al.: Early diagnosis of plant disease via NIR spectroscopy: a study in Bursaphelenchus Xylophilus disease. Int. J. Robot. Autom. 33 (2018). https://doi.org/10.2316/Journal.206.2018.3.206-5535

  8. Iqbal, Z., Khan, M.A., Sharif, M., Shah, J.H., ur Rehman, M.H., Javed, K.: An automated detection and classification of citrus plant diseases using image processing techniques: a review. Comput. Electron. Agric. 153, 12–32 (2018). https://doi.org/10.1016/j.compag.2018.07.032

    Article  Google Scholar 

  9. Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018). https://doi.org/10.1016/j.compag.2018.01.009

    Article  Google Scholar 

  10. Cui, S., Ling, P., Zhu, H., Keener, H.: Plant pest detection using an artificial nose system: a review. Sensors 18, 378 (2018). https://doi.org/10.3390/s18020378

    Article  Google Scholar 

  11. Aasha Nandhini, S., Hemalatha, R., Radha, S., Indumathi, K.: Web enabled plant disease detection system for agricultural applications using WMSN. Wireless Pers. Commun. 102, 725–740 (2018). https://doi.org/10.1007/s11277-017-5092-4

    Article  Google Scholar 

  12. Sun, G., Jia, X., Geng, T.: Plant diseases recognition based on image processing technology. J. Electr. Comput. Eng. 2018, 1–7 (2018). https://doi.org/10.1155/2018/6070129

    Article  MathSciNet  Google Scholar 

  13. Labaña, F.M., Ruiz, A., García-Sánchez, F.: PestDetect: pest recognition using convolutional neural network. In: Valencia-García, R., Alcaraz-Mármol, G., Cioppo-Morstadt, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds.) CITAMA2019 2019. AISC, vol. 901, pp. 99–108. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10728-4_11

    Chapter  Google Scholar 

  14. Garcerán-Sáez, J., García-Sánchez, F.: SePeRe: semantically-enhanced system for pest recognition. In: Valencia-García, R., Alcaraz-Mármol, G., Cioppo-Morstadt, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds.) CITAMA2019 2019. AISC, vol. 901, pp. 3–11. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10728-4_1

    Chapter  Google Scholar 

  15. Lagos-Ortiz, K., Medina-Moreira, J., Paredes-Valverde, M.A., Espinoza-Morán, W., Valencia-García, R.: An ontology-based decision support system for the diagnosis of plant diseases. J. Inf. Technol. Res. 10, 42–55 (2017). https://doi.org/10.4018/JITR.2017100103

    Article  Google Scholar 

  16. Gelbukh, A.: Introduction to the thematic issue on natural language processing. Computación y Sistemas 22, 721–727 (2018). https://doi.org/10.13053/cys-22-3-3032

    Article  Google Scholar 

  17. Chowdhury, G.G.: Natural language processing. Ann. Rev. Inf. Sci. Technol. 37, 51–89 (2005). https://doi.org/10.1002/aris.1440370103

    Article  Google Scholar 

  18. Paredes-Valverde, M.A., Valencia-García, R., Rodríguez-García, M.Á., Colomo-Palacios, R., Alor-Hernández, G.: A semantic-based approach for querying linked data using natural language. J. Inf. Sci. 42, 851–862 (2016). https://doi.org/10.1177/0165551515616311

    Article  Google Scholar 

  19. Endara, L., Burleigh, J.G., Cooper, L., Jaiswal, P., Laporte, M.-A., Cui, H.: A natural language processing pipeline to extract phenotypic data from formal taxonomic descriptions with a focus on flagellate plants. In: Jaiswal, P., Cooper, L., Haendel, M.A., Mungall, C.J. (eds.) Proceedings of the 9th International Conference on Biological Ontology (ICBO 2018), CEUR Workshop Proceedings 2285, Corvallis, Oregon, USA, pp. 1–4 (2018). http://www.CEUR-WS.org

  20. Sharma, V., Law, W., Balick, M.J., Sarkar, I.N.: Harnessing biomedical natural language processing tools to identify medicinal plant knowledge from historical texts. In: AMIA Annual Symposium Proceedings, Washington, DC, USA, pp. 1537–1546. American Medical Informatics Association (2017)

    Google Scholar 

  21. Dreisbach, C., Koleck, T.A., Bourne, P.E., Bakken, S.: A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. Int. J. Med. Informatics 125, 37–46 (2019). https://doi.org/10.1016/j.ijmedinf.2019.02.008

    Article  Google Scholar 

  22. Koleck, T.A., Dreisbach, C., Bourne, P.E., Bakken, S.: Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review. J. Am. Med. Inform. Assoc. 26, 364–379 (2019). https://doi.org/10.1093/jamia/ocy173

    Article  Google Scholar 

  23. Cambria, E., White, B.: Jumping NLP curves: a review of natural language processing research. IEEE Comput. Intell. Mag. 9, 48–57 (2014). https://doi.org/10.1109/MCI.2014.2307227

    Article  Google Scholar 

  24. Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13, 55–75 (2018). https://doi.org/10.1109/MCI.2018.2840738

    Article  Google Scholar 

  25. Academic and Open Source Natural Language Toolkits. http://alias-i.com/lingpipe/web/competition.html. Accessed 09 Aug 2019

  26. Ramos Gourcy, F.: Una lista de la gama de las aplicaciones móviles (“apps”) para la agricultura. https://www.hortalizas.com/proteccion-de-cultivos/61807/. Accessed 12 Aug 2019

  27. Lagos-Ortiz, K., Medina-Moreira, J., Sinche-Guzmán, A., Garzón-Goya, M., Vergara-Lozano, V., Valencia-García, R.: Mobile applications for crops management. In: Valencia-García, R., Alcaraz-Mármol, G., Del Cioppo-Morstadt, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds.) CITI 2018. CCIS, vol. 883, pp. 57–69. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00940-3_5

    Chapter  Google Scholar 

  28. Yue, Y., et al.: Deep recursive super resolution network with Laplacian Pyramid for better agricultural pest surveillance and detection. Comput. Electron. Agric. 150, 26–32 (2018). https://doi.org/10.1016/j.compag.2018.04.004

    Article  Google Scholar 

  29. Goodridge, W., Bernard, M., Jordan, R., Rampersad, R.: Intelligent diagnosis of diseases in plants using a hybrid multi-criteria decision making technique. Comput. Electron. Agric. 133, 80–87 (2017). https://doi.org/10.1016/j.compag.2016.12.003

    Article  Google Scholar 

  30. Patil, J.K., Kumar, R.: Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features. Eng. Agric. Environ. Food 10, 69–78 (2017). https://doi.org/10.1016/j.eaef.2016.11.004

    Article  Google Scholar 

  31. Zhang, S., Wu, X., You, Z., Zhang, L.: Leaf image based cucumber disease recognition using sparse representation classification. Comput. Electron. Agric. 134, 135–141 (2017). https://doi.org/10.1016/j.compag.2017.01.014

    Article  Google Scholar 

  32. Singh, V., Misra, A.K.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4, 41–49 (2017). https://doi.org/10.1016/j.inpa.2016.10.005

    Article  Google Scholar 

  33. del Águila, I.M., Cañadas, J., Túnez, S.: Decision making models embedded into a web-based tool for assessing pest infestation risk. Biosys. Eng. 133, 102–115 (2015). https://doi.org/10.1016/J.BIOSYSTEMSENG.2015.03.006

    Article  Google Scholar 

  34. Cañadas, J., del Águila, I.M., Palma, J.: Development of a web tool for action threshold evaluation in table grape pest management. Precision Agric. 18, 974–996 (2017). https://doi.org/10.1007/s11119-016-9487-0

    Article  Google Scholar 

  35. Fielding, R.T.: Architectural Styles and the Design of Network-based Software Architectures (2000). https://www.ics.uci.edu/~fielding/pubs/dissertation/top.htm

  36. Krasner, G.E., Pope, S.T.: A cookbook for using the model-view controller user interface paradigm in smalltalk-80. J. Object-Oriented Program. 1, 26–49 (1988)

    Google Scholar 

  37. Ministerio de Agricultura, Alimentación y Medio Ambiente: Guía de Gestión Integrada de Plagas. Almendro. https://www.mapa.gob.es/es/agricultura/temas/sanidad-vegetal/guiadealmendroweb_tcm30-57951.pdf. Accessed 12 Aug 2019

  38. Ministerio de Medio Ambiente y Medio Rural y Marino: Patógenos de plantas descritos en España. Sociedad Española de Fitopatología, Madrid, Spain (2010)

    Google Scholar 

  39. Lagos-Ortiz, K., Medina-Moreira, J., Morán-Castro, C., Campuzano, C., Valencia-García, R.: An ontology-based decision support system for insect pest control in crops. In: Valencia-García, R., Alcaraz-Mármol, G., Del Cioppo-Morstadt, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds.) CITI 2018. CCIS, vol. 883, pp. 3–14. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00940-3_1

    Chapter  Google Scholar 

  40. García-Sánchez, F., García-Díaz, J.A., Gómez-Berbís, J.M., Valencia-García, R.: Financial knowledge instantiation from semi-structured, heterogeneous data sources. In: Silhavy, R. (ed.) CSOC2018 2018. AISC, vol. 764, pp. 103–110. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91189-2_11

    Chapter  Google Scholar 

Download references

Acknowledgements

This work has been partially supported by the Spanish National Research Agency (AEI) and the European Regional Development Fund (FEDER/ERDF) through project KBS4FIA (TIN2016-76323-R), and Seneca Foundation-the Regional Agency for Science and Technology of Murcia (Spain)- through project 20963/PI/18.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Francisco García-Sánchez .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hernández-Castillo, C., Guedea-Noriega, H.H., Rodríguez-García, M.Á., García-Sánchez, F. (2019). Pest Recognition Using Natural Language Processing. In: Valencia-García, R., Alcaraz-Mármol, G., Del Cioppo-Morstadt, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds) Technologies and Innovation. CITI 2019. Communications in Computer and Information Science, vol 1124. Springer, Cham. https://doi.org/10.1007/978-3-030-34989-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34989-9_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34988-2

  • Online ISBN: 978-3-030-34989-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics