Skip to main content
SpringerLink
Log in

An Image-Based Deep Learning Model for Cannabis Diseases, Nutrient Deficiencies and Pests Identification

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11804))

Included in the following conference series:

Abstract

In this work, a deep learning system for cannabis plants disease, nutrient deficiencies and pests identification is developed, based on image data processed by convolutional neural network models. Training of the models was performed using image data available on the Internet, while database development included data cleansing by expert agronomists, basic image editing, and data augmentation techniques commonly used in deep learning applications in order to expand the rather limited amount of available data. Three fungi diseases, two pests and three nutrient deficiencies were included in the identification system, together with healthy plants identification. The final model reached a performance of 90.79% in successfully identifying cannabis diseases (or healthy plants) in previously “unseen” plant images. The most difficult cannabis problems to be identified were powdery mildew and potassium deficiency. Results showed that transfer learning from existing models specialized in similar tasks to the one under development, is more successful than using transfer learning from more general models. Finally, even though the amount of training images in some of the considered problems was significantly small, no correlation between model performance and the size of the training dataset for each category was found.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Institutional subscriptions

References

  1. Andre, C.M., Hausman, J.-F., Guerriero, G.: Cannabis sativa: the plant of the thousand and one molecules. Front. Plant Sci. 7, 19 (2016)

    Article  Google Scholar 

  2. Clarke, R.C., Merlin, M.D.: Cannabis: Evolution and Ethnobotany. University of California Press, Los Angeles and Berkeley (2013)

    Google Scholar 

  3. Clarke, R.C.: Marijuana Botany: An Advanced Study: The Propagation and Breeding of Distinctive Cannabis. Ronin Publishing, Oakland (1981)

    Google Scholar 

  4. Small, E.: Cannabis, a Complete Guide. CRC Press, Boca Raton (2017)

    Google Scholar 

  5. FAOSTAT. http://www.fao.org/faostat/en/#compare. Accessed 04 Apr 2019

  6. Punja, Z.K.: Flower and foliage-infecting pathogens of marijuana (Cannabis sativa L.) plants. Can. J. Plant Pathol. 40(4), 514–527 (2018)

    Article  Google Scholar 

  7. Tyagi, A.C.: Towards a second green revolution. Irrig. Drainage 65(4), 388–389 (2016)

    Article  Google Scholar 

  8. Gebbers, R., Adamchuk, V.I.: Precision agriculture and food security. Science 327, 828–831 (2010)

    Article  Google Scholar 

  9. Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)

    Article  Google Scholar 

  10. Yang, X., Guo, T.: Machine learning in plant disease research. Eur. J. BioMed. Res. 3(1), 6–9 (2017)

    Article  Google Scholar 

  11. Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018)

    Article  Google Scholar 

  12. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)

    Article  Google Scholar 

  13. Kamilaris, A., Prenafeta-Boldú, F.: Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70–90 (2018)

    Article  Google Scholar 

  14. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  15. Dan, C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, vol. 2, pp. 1237–1242 (2011)

    Google Scholar 

  16. Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 29(6), 82–97 (2012)

    Article  Google Scholar 

  17. LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks, vol. 3361(10). MIT Press (1995)

    Google Scholar 

  18. Grinblat, G.L., Uzal, L.C., Larese, M., Granitto, P.: Deep learning for plant identification using vein morphological patterns. Comput. Electron. Agric. 127, 418–424 (2016)

    Article  Google Scholar 

  19. Dyrmann, M., Jorgensen, R.N., Midtiby, H.S.: Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network. In: 11th European Conference on Precision Agriculture, pp. 842–847 (2017)

    Google Scholar 

  20. Dyrmann, M., Skovsen, S., Laursen, M.S., Jorgensen, R.N.: Using a fully convolutional neural network for detecting locations of weeds in images from cereal fields. In: 14th International Conference on Precision Agriculture, Montreal, Quebec, Canada (2018)

    Google Scholar 

  21. Cheng, X., Zhang, Y., Chen, Y., Wu, Y., Yue, Y.: Pest identification via deep residual learning in complex background. Comput. Electron. Agric. 141, 351–356 (2017)

    Article  Google Scholar 

  22. Toda Y., Okura F.: How convolutional neural networks diagnose plant disease. Plant Phenomics 2019(9237136) (2019)

    Article  Google Scholar 

  23. Barbedo, J.G.A.: Factors influencing the use of deep learning for plant disease recognition. Biosyst. Eng. 172, 84–91 (2018)

    Article  Google Scholar 

  24. Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 2016, 11 (2016)

    Article  Google Scholar 

  25. Pawara, P., Okafor, E., Surinta, O., Schomaker, L., Wiering, M.: Comparing local descriptors and bags of visual words to deep convolutional neural networks for plant recognition. In: 6th International Conference on Pattern Recognition Applications and Methods (2017)

    Google Scholar 

  26. Lin, K., Gong, L., Huang, Y., Liu, C., Pan, J.: Deep learning-based segmentation and quantification of cucumber powdery mildew using convolutional neural network. Front. Plant Sci. 10, 155 (2019)

    Article  Google Scholar 

  27. Barbedo, J.G.A.: Plant disease identification from individual lesions and spots using deep learning. Biosyst. Eng. 180, 96–107 (2019)

    Article  Google Scholar 

  28. An, J., Li, W., Li, M., Cui, S., Yue, H.: Identification and classification of maize drought stress using deep convolutional neural network. Symmetry 11(2), 256 (2019)

    Article  Google Scholar 

  29. McPartland, J.M.: A review of Cannabis diseases. J. Int. Hemp Assoc. 3(1), 19–23 (1996)

    Google Scholar 

  30. McPartland, J.M.: Cannabis pathogens XI: Septoria spp. on Cannabis sativa, sensu strico. Sydowia 47, 44–53 (1995)

    Google Scholar 

  31. McPartland, J.M.: Cannabis pathogens X: Phoma, Ascochyta and Didymella species. Mycologia 86, 870–878 (1995)

    Article  Google Scholar 

  32. McPartland, J.M.: Common names for diseases of Cannabis sativa L. Plant Dis. 75, 226–227 (1991)

    Google Scholar 

  33. Thompson, G.R., et al.: A microbiome assessment of medical marijuana. Clin. Microbiol. Infect. 23(4), 269–270 (2017)

    Article  Google Scholar 

  34. Frank, M.: Marijuana Grower’s Insider’s Guide. Red Eye Press, Los Angeles (1988)

    Google Scholar 

  35. McPartland, J.M., Clarke, R.B., Watson, D.P.: Hemp Diseases and Pests Management and Biological Control. CABI Publishing, United Kingdom (2000)

    Book  Google Scholar 

  36. McPartland, J.M.: Cannabis pests. J. Int. Hemp Assoc. 3(2), 49–52 (1996)

    Google Scholar 

  37. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  38. Krizhevsky, A.: One weird trick for parallelizing convolutional neural networks arXiv:1404.5997 (2014)

  39. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  40. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks arXiv:1312.6229 (2013)

  41. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition arXiv:1409.1556 (2014)

  42. Bengio, Y.: Learning deep architectures for AI. Found. Trends® Mach. Learn. 2(1), 1–127 (2009)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK-02182).

Author information

Authors and Affiliations

  1. Department of Agricultural Engineering Soil and Water Resources Institute, Hellenic Agricultural Organization “Demeter”, Athens, Greece

    Konstantinos P. Ferentinos & Myrto Barda

  2. Testfire Labs, Edmonton, Canada

    Dave Damer

Authors
  1. Konstantinos P. Ferentinos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Myrto Barda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dave Damer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Konstantinos P. Ferentinos .

Editor information

Editors and Affiliations

  1. INESC-TEC, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    Paulo Moura Oliveira

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. LIACC/UP, University of Porto, Porto, Portugal

    Luís Paulo Reis

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

Ferentinos, K.P., Barda, M., Damer, D. (2019). An Image-Based Deep Learning Model for Cannabis Diseases, Nutrient Deficiencies and Pests Identification. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30241-2_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30240-5

  • Online ISBN: 978-3-030-30241-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation