Devising a method for recognizing the causes of deviations in the development of the plant Aloe arborescens L. using machine learning capabilities

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.228219

Keywords:

neural network, machine learning, hydroponic systems, image recognition, Aloe arborescens L.

Abstract

This paper considers the process of developing a method to recognize the causes of plant growth deviations from normal using the advancements in artificial intelligence. The medicinal plant Aloe arborescens L. was chosen as the object of this research given that this plant had been for decades one of the best-selling new products in the world. Aloe arborescens L. is famous for its medicinal properties used in medicine, cosmetology, and even the food industry. Diagnosing the abnormalities in the plant development in a timely and accurate manner plays an important role in preventing the loss of crop production yields.

The current study has built a method for recognizing the causes of abnormalities in the development of Aloe arborescens L. caused by a lack of watering or lighting, based on the use of transfer training of the VGG-16 convolutional neural network (United Kingdom). A given architecture is aimed at recognizing objects in images, which is the main reason for using it to achieve the goal set.

The analysis of the quality metrics of the proposed image classification process by specified classes has revealed high recognition reliability (for a normally developing plant, 91 %; for a plant without proper watering, 89 %; and for a plant without proper lighting, 83 %). The analysis of the validity of test sample recognition has demonstrated a similar validity of the plant's classification to one of three classes: 92.6 %; 87.5 %; and 85.5 %, respectively.

The results reported here make it possible to supplement the automated systems that control the mode parameters of hydroponic installations by the world's major producers with the main feedback on the deviation of the plant's development from the specified values

Author Biographies

Gulnar Kim, Manash Kozybayev North Kazakhstan University

Technical Sciences Master

Department of Information and Communication Technologies

Alexandr Demyanenko, Manash Kozybayev North Kazakhstan University

PhD

Department of Energetic and Radioelectronics

Alexey Savostin, Manash Kozybayev North Kazakhstan University

PhD, Associate Professor

Department of Energetic and Radioelectronics

Kainizhamal Iklassova, Manash Kozybayev North Kazakhstan University

PhD, Associate Professor

Department of Information and Communication Technologies

References

  1. Tikhomirova, L. I., Bazarnova, N. G., Il'icheva, T. N., Sysoeva, A. V. (2016). Process for the preparation of medicinal plants of Potentilla alba (Potentilla alba L.) under hydroponics. Chemistry of Plant Raw Material, 3, 59–66. doi: https://doi.org/10.14258/jcprm.2016031228
  2. Vardanyan, A. P. (2009) Sopryazhennyy metod kul'tury in vitro i gidroponiki dlya sohraneniya prirodnyh resursov Hypericum perforatum. Flora, rastitel'nost' i rastitel'nye resursy Armenii, 17, 108–110. Available at: http://takhtajania.asj-oa.am/392/
  3. Dorais, M., Papadopoulos, A. P., Luo, X., Leonhart, S., Gosselin, A., Pedneault, K. et. al. (2001). Soilless Greenhouse Production of Medicinal Plants in North Eastern Canada. Acta Horticulturae, 554, 297–304. doi: https://doi.org/10.17660/actahortic.2001.554.32
  4. Ustanovka "Podsolnuh". Available at: https://gidroponika.com/content/view/330/142/
  5. AeroFlo 14. Available at: https://www.gidroponika.su/g/gidroponnye-ustanovki/ghe-sistema/aeroflo-14-kupit.html
  6. Kim, G., Demyanenko A. (2019). A review of automated hydroponic systems of the main world manufacturers. VESTNIK KazNRTU, 4 (134), 370–376. Available at: https://official.satbayev.university/download/document/12092/%D0%92%D0%95%D0%A1%D0%A2%D0%9D%D0%98%D0%9A-2019%20%E2%84%964.pdf
  7. Villanueva, M. B., Salenga, M. L. M. (2018). Bitter Melon Crop Yield Prediction using Machine Learning Algorithm. International Journal of Advanced Computer Science and Applications, 9 (3). doi: https://doi.org/10.14569/ijacsa.2018.090301
  8. Türkoğlu, M., Hanbay, D. (2019). Plant disease and pest detection using deep learning-based features. Turkish Journal of Electrical Engineering & Computer Sciences, 27 (3), 1636–1651. doi: https://doi.org/10.3906/elk-1809-181
  9. Amara, J., Bouaziz, B., Algergawy, A. (2017). A Deep Learning-based Approach for Banana Leaf Diseases Classification. Conference: Datenbanksysteme für Business, Technologie und Web (BTW 2017). Bonn: Gesellschaft für Informatik e.V., 79–88. Available at: https://dl.gi.de/bitstream/handle/20.500.12116/944/paper09.pdf?sequence=1&isAllowed=y
  10. Ishikawa, T., Hayashi, A., Nagamatsu, S., Kyutoku, Y., Dan, I., Wada, T. et. al. (2018). Classification of strawberry fruit shape by machine learning. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2, 463–470. doi: https://doi.org/10.5194/isprs-archives-XLII-2-463-2018
  11. Ullah, A., Mohd Nawi, N., Arifianto, A., Ahmed, I., Aamir, M., Khan, S. N. (2019). Real-Time Wheat Classification System for Selective Herbicides Using Broad Wheat Estimation in Deep Neural Network. International Journal on Advanced Science, Engineering and Information Technology, 9 (1), 153. doi: https://doi.org/10.18517/ijaseit.9.1.5031
  12. Wang, H., Li, G., Ma, Z., Li, X. (2012). Application of neural networks to image recognition of plant diseases. 2012 International Conference on Systems and Informatics (ICSAI2012). doi: https://doi.org/10.1109/icsai.2012.6223479
  13. ChandraKarmokar, B., Samawat Ullah, M., Kibria Siddiquee, M., Md. Rokibul Alam, K. (2015). Tea Leaf Diseases Recognition using Neural Network Ensemble. International Journal of Computer Applications, 114 (17), 27–30. doi: https://doi.org/10.5120/20071-1993
  14. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S. et. al. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115 (3), 211–252. doi: https://doi.org/10.1007/s11263-015-0816-y
  15. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86 (11), 2278–2324. doi: https://doi.org/10.1109/5.726791
  16. Krizhevsky, A., Sutskever, I., Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60 (6), 84–90. doi: https://doi.org/10.1145/3065386
  17. LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521 (7553), 436–444. doi: https://doi.org/10.1038/nature14539
  18. Srivastava, R. K., Greff, K., Schmidhuber, J. (2015). Training Very Deep Networks. arXiv. Available at: https://arxiv.org/pdf/1507.06228.pdf
  19. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D. et. al. (2015). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi: https://doi.org/10.1109/cvpr.2015.7298594
  20. He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi: https://doi.org/10.1109/cvpr.2016.90
  21. Du, W., Rao, N., Liu, D., Jiang, H., Luo, C., Li, Z. et. al. (2019). Review on the Applications of Deep Learning in the Analysis of Gastrointestinal Endoscopy Images. IEEE Access, 7, 142053–142069. doi: https://doi.org/10.1109/access.2019.2944676
  22. VGG16 model for Keras. Available at: https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3
  23. Large Scale Visual Recognition Challenge 2012. Available at: http://image-net.org/challenges/LSVRC/2012/
  24. Keras. Available at: https://keras.io/
  25. TensorFlow. Available at: https://www.tensorflow.org/
  26. Theano. Available at: https://github.com/Theano/Theano
  27. The Microsoft Cognitive Toolkit (2017). Available at: https://docs.microsoft.com/en-us/cognitive-toolkit
  28. NVIDIA cuDNN. Available at: https://developer.nvidia.com/cudnn
  29. Nikolenko, S., Kadurin, A., Arhangel'skaya, E. (2018). Glubokoe obuchenie. Sankt-Peterburg: Piter, 480.
  30. Kingma, D. P., Ba, J. (2014). Adam: A Method for Stochastic Optimization. International Conference on Learning Representations. arXiv. Available at: https://arxiv.org/pdf/1412.6980.pdf

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Published

2021-04-30

How to Cite

Kim, G., Demyanenko, A., Savostin, A., & Iklassova, K. (2021). Devising a method for recognizing the causes of deviations in the development of the plant Aloe arborescens L. using machine learning capabilities . Eastern-European Journal of Enterprise Technologies, 2(2 (110), 23–31. https://doi.org/10.15587/1729-4061.2021.228219

Issue

Vol. 2 No. 2 (110) (2021): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2021 Gulnar Kim, Alexandr Demyanenko, Alexey Savostin, Kainizhamal Iklassova

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