Pest Recognition Using Natural Language Processing

  • Carlos Hernández-Castillo
  • Héctor Hiram Guedea-Noriega
  • Miguel Ángel Rodríguez-García
  • Francisco García-SánchezEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1124)


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.


Pest recognition Natural Language Processing Integrated Pest Management 



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.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DIS, Faculty of Computer ScienceUniversity of MurciaMurciaSpain
  2. 2.Escuela Internacional de DoctoradoUniversity of MurciaMurciaSpain
  3. 3.Universidad Rey Juan CarlosMadridSpain

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