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

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Progress in Artificial Intelligence (EPIA 2019)

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

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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.

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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).

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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
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  2. Myrto Barda
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  3. Dave Damer
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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

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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

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

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