PestDetect: Pest Recognition Using Convolutional Neural Network

  • Federico Murcia Labaña
  • Alberto Ruiz
  • Francisco García-SánchezEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 901)


Agriculture is a strategic sector in many regions around the world. In those regions where water scarcity is an endemic problem, crops tend to suffer hydric stress which make them prone to suffer from pests and diseases. Thus, periodic checks to detect those pests are crucial to prevent and act upon them on early stages. Portable smart devices like phone mobiles or tablets offer Internet connectivity and camera devices. These two properties make them a potential tool that can be used for this work: to make an in situ early detection of the pest or disease that could help to reduce the negative impact of these on the affected crop and so minimize economic loss. In this work we propose an application prototype that can issue a diagnosis and its related treatment from a photograph of an affected crop taken by the user anytime/anywhere. This is achieved by using a combination of different technologies such as Convolutional Neural Networks and REST services, among others. The first tests with a reduced set of crops and diseases resulted in an accuracy over 90%.


Convolutional neural network Diseases classifier Plant pest recognition 


  1. 1.
    Ayres, P.G.: Water relations of diseased plants. In: Water and Plant Disease, pp. 1–60. Elsevier (1978)Google Scholar
  2. 2.
    CETAQUA: Artificial intelligence for agricultural water demand forecasting in South-Eastern Spain. (2018). Accessed 30 Sept 2018
  3. 3.
    Khan, S., Rahmani, H., Shah, S.A.A., Bennamoun, M.: A guide to convolutional neural networks for computer vision. Synth. Lect. Comput. Vis. 8, 1–207 (2018)CrossRefGoogle Scholar
  4. 4.
    Prasad, S., Peddoju, S.K., Ghosh, D.: Multi-resolution mobile vision system for plant leaf disease diagnosis. Signal Image Video Process. 10, 379–388 (2016)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Sun, G., Jia, X., Geng, T.: Plant diseases recognition based on image processing technology. J. Electr. Comput. Eng. 2018, 1–7 (2018)MathSciNetGoogle Scholar
  7. 7.
    Chollet, F.: Deep Learning with Python. Manning Publications (2017)Google Scholar
  8. 8.
    Keras. (2018). Accessed 30 Sept 2018
  9. 9.
    TensorFlow. (2018). Accessed 30 Sept 2018
  10. 10.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference for Learning Representations, San Diego (2015)Google Scholar
  11. 11.
    Hu, K., et al.: Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing 309, 179–191 (2018)CrossRefGoogle Scholar
  12. 12.
    Bootstrap. (2018). Accessed 30 Sept 2018
  13. 13.
    Fielding, R.T.: Architectural Styles and the Design of Network-based Software Architectures. University of California, Irvine (2000)Google Scholar
  14. 14.
    Java. (2018). Accessed 30 Sept 2018
  15. 15.
    Apache Tomcat. (2018). Accessed 30 Sept 2018
  16. 16.
    MySQL. (2018). Accessed 30 Sept 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Computer Science, Department of Informatics and SystemsUniversity of MurciaMurciaSpain

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