Cover
Vol. 18 No. 2 (2022)

Published: December 31, 2022

Pages: 1-8

Review Article

Agriculture based on Internet of Things and Deep Learning

Marwa Abdulla 1 Ali Marhoon 2
1Computer Science, College of Education, University of Basrah, Basrah, Iraq
2Electrical Department, College of Engineering, University of Basrah, Basrah, Iraq
History
  • Received: December 21, 2021
  • Revised: February 8, 2022
  • Accepted: February 8, 2022
  • Available Online: May 17, 2022
DOI

https://doi.org/10.37917/ijeee.18.2.1

Abstract

In smart cities, health care, industrial production, and many other fields, the Internet of Things (IoT) have had significant success. Protected agriculture has numerous IoT applications, a highly effective style of modern agriculture development that uses artificial ways to manipulate climatic parameters such as temperature to create ideal circumstances for the growth of animals and plants. Convolutional Neural Networks (CNNs) is a deep learning approach that has made significant progress in image processing. From 2016 to the present, various applications for the automatic diagnosis of agricultural diseases, identifying plant pests, predicting the number of crops, etc., have been developed. This paper involves a presentation of the Internet of Things system in agriculture and its deep learning applications. It summarizes the most essential sensors used and methods of communication between them, in addition to the most important deep learning algorithms devoted to intelligent agriculture.

Keywords

References

  1. B. Faithpraise and C. Chatwin, "Automatic Plant Pest Detection And Recognition Using K-Means Clustering Algorithm And Correspondence Filters," vol. 4, no. 2, pp. 189–199, 2013.
  2. N. Soontranon, P. Tangpattanakul, P. Srestasathiern, and P. Rakwatin, "An Agricultural Monitoring System : Field Server Data Collection and Analysis on Paddy Field," 14th International Symposium on Communications and Information Technologies (ISCIT), 2014.
  3. P. P. Jayaraman and D. Palmer, "Do-it-Yourself Digital Agriculture Applications with Semantically Enhanced loT Platform," no. April 2015, pp. 7–9, 2020.
  4. P. P. Jayaraman, A. Yavari, D. Georgakopoulos, A. Morshed, A. Zaslavsky, "Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt. Sensors", Vol. 16, 2016. https://doi.org/10.3390/s16111884.
  5. S. Navulur, A. S. C. S. Sastry, and M. N. G. Prasad, "Agricultural Management through Wireless Sensors and Internet of Things," International Journal of Electrical and Computer Engineering (IJECE), Vol. 7, No. 6, 2017. doi: 10.11591/ijece.v7i6.pp3492-3499.
  6. G. Wang, Y. Sun, and J. Wang, "Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning," Computational Intelligence and Neuroscience, vol. 2017, 2017.
  7. M. Bacco et al., "Smart Farming : Opportunities , Challenges and Technology Enablers," IoT Vertical and Topical Summit on Agriculture - Tuscany (IOT Tuscany), 2018.
  8. R. Yakkundimath, G. Saunshi, and V. Kamatar, "Plant Disease Detection using IoT," vol. 8, no. 9, 2018.
  9. A. Kochhar and N. Kumar, "Wireless sensor networks for greenhouses : An end-to-end review Number of papers in IEEE digital library Number of papers in Springer," vol. 163, 2019, doi: 10.1016/j.compag.2019.104
  10. X. Shi, X. An, Q. Zhao, H. Liu, and L. Xia, "State-of-the-Art Internet of Things in Protected Agriculture," Sensors 2019, Vol. 19, No. 8, 2019.
  11. J. Boulent, S. Foucher, and J. Théau, "Convolutional Neural Networks for the Automatic Identification of Plant Diseases," vol. 10, no. July, 2019, doi: 10.3389/fpls.2019.00941.
  12. R. D. Devi, S. A. Nandhini, R. Hemalatha, and S. Radha, "IoT enabled efficient detection and classification of plant diseases for agricultural applications," Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2019, pp. 447–451, 2019, doi: 10.1109/WiSPNET45539.2019.9032727.
  13. M. Bayram, A. Sciences, T. Faculty, and E. Engineering, "Wireless sensor network based remote drip irrigation system," vol. 6, no. 4, pp. 554–563, 2018, doi: 10.21923/jesd.443576.
  14. M. A. Nawaz et al., "Plant disease detection using internet of thing (IoT)," Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 1, pp. 505–509, 2020, doi: 10.14569/ijacsa.2020.0110162.
  15. F. A. Khan, A. A. Ibrahim, and A. M. Zeki, "Environmental monitoring and disease detection of plants in smart greenhouse using internet of things," J. Phys. Commun., vol. 4, no. 5, 2020, doi: 10.1088/2399-6528/ab90c1.
  16. J. Liu and X. Wang, "Plant diseases and pests detection based on deep learning : a review," Plant Methods, Vol. 17, No. 22, 2021, pp. 1–18, 2021.
  17. M. R. P. Fuke and N. V Raut, "IOT Based Solution for Leaf Disease Prediction," International Journal of Innovative Research In Computing, pp. 76–81, 2020.
  18. C. Vision, "Computer Vision , IoT and Data Fusion for Crop Disease Detection Using Machine Learning : A Survey and Ongoing Research," Remote Sensing, Vol. 13, No. 13, 2021.
  19. A. Kamilaris and F. X. Prenafeta-Boldú, "Deep learning in agriculture: A survey," Comput. Electron. Agric., vol. 147, pp. 70–90, 2018. doi: 10.1016/j.compag.2018.02.016.
  20. S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, "Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification," Comput. Intell. Neurosci., vol. 2016, 2016. doi: 10.1155/2016/3289801.
  21. M. Rahnemoonfar and C. Sheppard, "Deep count: Fruit counting based on deep simulated learning," Sensors (Switzerland), vol. 17, no. 4, pp. 1–12, 2017. doi: 10.3390/s17040905.
  22. J. A. López Riquelme, F. Soto, J. Suardíaz, P. Sánchez, A. Iborra, and J. A. Vera, “Wireless Sensor Networks for precision horticulture in Southern Spain,” Comput. Electron. Agric., vol. 68, no. 1, pp. 25–35, 2009, doi: 10.1016/j.compag.2009.04.006.
  23. D. H. Park and J. W. Park, "Wireless sensor network-based greenhouse environment monitoring and automatic control system for dew condensation prevention," Sensors, vol. 11, no. 4, pp. 3640–3651, 2011, doi: 10.3390/s110403640.
  24. S. S. Patil and S. A. Thorat, "Early detection of grapes diseases using machine learning and IoT," Proc. - 2016 2nd Int. Conf. Cogn. Comput. Inf. Process. CCIP 2016, 2016, doi: 10.1109/CCIP.2016.7802887.
  25. P. B. Padol and A. A. Yadav, "SVM classifier based grape leaf disease detection," Conf. Adv. Signal Process. CASP 2016, pp. 175–179, 2016, doi: 10.1109/CASP.2016.7746160.
  26. Ç. Ersin, R. Gürbüz, and A. K. Yakut, "Application of an automatic plant irrigation system based arduino microcontroller using solar energy," Solid State Phenom., vol. 251, pp. 237–241, 2016, doi: 10.4028/www.scientific.net/SSP.251.237.
  27. H. Wani, "An Appropriate Model Predicting Pest / Diseases of Crops Using Machine Learning Algorithms," 4th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 4–7, 2017.
  28. S. Yadav, N. Sengar, A. Singh, A. Singh, and M. K. Dutta, Abdulla & Marhoon "Identification of disease using deep learning and evaluation of bacteriosis in peach leaf," Ecol. Inform., vol. 61, p. 101247, 2021, doi: 10.1016/j.ecoinf.2021.101247.
  29. V. Tiwari, R. C. Joshi, and M. K. Dutta, "Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images," Ecol. Inform., vol. 63, no. March, p. 101289, 2021, doi: 10.1016/j.ecoinf.2021.101289.
  30. V. Udutalapally, S. P. Mohanty, V. Pallagani, and V. Khandelwal, "sCrop: A Internet-of-Agro-Things (IoAT) Enabled Solar Powered Smart Device for Automatic Plant Disease Prediction," ArXiv, abs/2005.06342, pp. 1–23, 2020.
  31. G. Bbass and A. Marhoon, “Iraqi License Plate Detection and SegmentAation based on Deep Learning,” Iraqi J. Electr. Electron. Eng., vol. 17, no. 2, pp. 102–107, 2021, doi: 10.37917/ijeee.17.2.12.