Cover
Vol. 19 No. 1 (2023)

Published: June 30, 2023

Pages: 70-78

Original Article

Deep learning and IoT for Monitoring Tomato Plant

Marwa Abdulla 1 Ali Marhoon
1Computer Science, College of Education, University of Basrah, Basrah, Iraq
History
  • Received: September 9, 2022
  • Revised: October 2, 2022
  • Accepted: October 2, 2022
  • Available Online: January 20, 2023
DOI

https://doi.org/10.37917/ijeee.19.1.9

Abstract

Agriculture is the primary food source for humans and livestock in the world and the primary source for the economy of many countries. The majority of the country's population and the world depend on agriculture. Still, at present, farmers are facing difficulty in dealing with the requirements of agriculture. Due to many reasons, including different and extreme weather conditions, the abundance of water quality, etc. This paper applied the Internet of Things and deep learning system to establish a smart farming system to monitor the environmental conditions that affect tomato plants using a mobile phone. Through deep learning networks, trained the dataset taken from PlantVillage and collected from google images to classify tomato diseases, and obtained a test accuracy of 97%, which led to the publication of the model to the mobile application for classification for its high accuracy. Using the IoT, a monitoring system and automatic irrigation were built that were controlled through the mobile remote to monitor the environmental conditions surrounding the plant, such as air temperature and humidity, soil moisture, water quality, and carbon dioxide gas percentage. The designed system has proven its efficiency when tested in terms of disease classification, remote irrigation, and monitoring of the environmental conditions surrounding the plant. And giving alerts when the values of the sensors exceed the minimum or higher values causing damage to the plant. The farmer can take the appropriate action at the right time to prevent any damage to the plant and thus obtain a high-quality product.

Keywords

References

  1. Jaradat, “Agriculture in Iraq: Resources, potentials, constraints, research needs and priorities,” Agriculture, p.83, 2003. [Online]. Available: http://ddr.nal.usda.gov/dspace/handle/10113/47863.
  2. H. Abbas Drebee and N. Azam Abdul-Razak, “The Impact of Corruption on Agriculture Sector in Iraq: Econometrics Approach,” IOP Conf. Ser. Earth Environ. Sci., vol. 553, no. 1, 2020 doi: 10.1088/1755- 1315/553/1/012019.
  3. M. Abdulla and A. Marhoon, “Agriculture based on Internet of Things and Deep Learning,” vol. 18, Issue 2, pp. 1–8, 2022. doi: 10.37917/ijeee.18.2.1.
  4. M. S. Farooq, S. Riaz, A. Abid, K. Abid, and M. A. Naeem, “A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming,” IEEE Access, vol. 7, pp. 156237–156271, 2019. doi: 10.1109/ACCESS.2019.2949703.
  5. O. Elijah, S. Member, T. A. Rahman, and I. Orikumhi, “An Overview of Internet of Things ( IoT ) and Data Analytics in Agriculture : Benefits and Challenges,” vol. 4662, no. c, pp. 1–17, 2018. doi: 10.1109/JIOT.2018.2844296.
  6. V. Meshram, K. Patil, V. Meshram, D. Hanchate, and S. D. Ramkteke, “Machine learning in agriculture domain: A state-of-art survey,” Artif. Intell. Life Sci., vol. 1, no. October, p. 100010, 2021. doi: 10.1016/j.ailsci.2021.100010.
  7. K. Lova Raju and V. Vijayaraghavan, "IoT Technologies in Agricultural Environment: A Survey", Springer US, vol. 113, no. 4. 2020.
  8. J. Ye, B. Chen, Q. Liu, and Y. Fang, “A Precision Agriculture Management System Based on Internet of Things and WebGIS,” 2013 21st International Conference on Geoinformatics, 2013.
  9. R. Mathur, V. Pathak, and D. Bandil, "Emerging Trends in Expert Applications and Security", Proceedings of ICETEAS 2018, 2019.
  10. M. Mannan J, S. Kanimozhi Suguna, M. Dhivya, and T. Parameswaran, “Smart scheduling on cloud for IoT- based sprinkler irrigation,” Int. J. Pervasive Comput. Commun., vol. 17, no. 1, pp. 3–19, 2021, doi: 10.1108/IJPCC-03-2020-0013.
  11. D. Sehrawat and N. S. Gill, “Smart sensors: Analysis of different types of IoT sensors,” Proc. Int. Conf. Trends Electron. Informatics, ICOEI 2019, no. Icoei, pp. 523– 528, 2019, doi: 10.1109/ICOEI.2019.8862778.
  12. P. Daponte et al., “A review on the use of drones for precision agriculture,” IOP Conf. Ser. Earth Environ. Sci., vol. 275, no. 1, 2019, doi: 10.1088/1755- 1315/275/1/012022.
  13. B. Faithpraise and C. Chatwin, "Automatic Plant Pest Detection And Recognition Using K-Means Clustering Algorithm And Correspondence Filters," International Journal of Advanced Biotechnology and Research, vol. 4, no. 2, pp. 189–199, 2013.
  14. 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.
  15. A. Uzhinskiy, G. Ososkov, P. Goncharov, and A. Nechaevskiy, “Multifunctional platform and mobile application for plant disease detection,” CEUR Workshop Proc., vol. 2507, pp.110–114, 2019.
  16. J. C. Valdoria, A. R. Caballeo, B. I. D. Fernandez, and J. M. M. Condino, “IDahon: An Android Based Terrestrial Plant Disease Detection Mobile Application Through Digital Image Processing Using Deep Learning Neural Network Algorithm,” Proc. 2019 4th Int. Conf. Inf. Technol. Encompassing Intell. Technol. Innov.Towar. New Era Hum. Life, InCIT 2019, pp. 94–98, 2019 doi: 10.1109/INCIT.2019.8912053.
  17. A. Smetanin, A. Uzhinskiy, G. Ososkov, P. Goncharov, and A. Nechaevskiy, “Deep learning methods for the plant disease detection platform,” AIP Conf. Proc., vol. 2377, no. September, 2021, doi: 10.1063/5.0068797.
  18. A. O. Adedoja, P. A. Owolawi, T. Mapayi, and C. Tu, “Intelligent Mobile Plant Disease Diagnostic System UsingNASNet-Mobile Deep Learning,” IAENG Int. J. Comput. Sci., vol. 49, no. 1, pp. 216–231, 2022.
  19. M. A. Jasim and J. M. Al-Tuwaijari, “Plant Leaf Diseases Detection and Classification Using Image Processing and Deep Learning Techniques,” Proc. 2020 Int. Conf. Comput. Sci. Softw. Eng. CSASE 2020, pp. 259– 265, 2020, doi: 10.1109/CSASE48920.2020.9142097.
  20. J. Muangprathub, N. Boonnam, S. Kajornkasirat, N. Lekbangpong, A. Wanichsombat, and P. Nillaor, “IoT and agriculture data analysis for smart farm,” Comput. Electron. Agric., vol. 156, no. June 2018, pp.467474,2019. doi: 10.1016/j.compag.2018.12.011.
  21. J. Kwok and Y. Sun, “A smart IoT-based irrigation system with automated plant recognition using deep learning,” ACM Int. Conf. Proceeding Ser., pp. 87–91, 2018, doi: 10.1145/3177457.3177506.
  22. P. M. Jacob, S. Suresh, J. M. John, P. Nath, P. Nandakumar, and S. Simon, “An Intelligent Agricultural Field Monitoring and Management System using Internet of Things and Machine Learning,”2020 Int. Conf. Data Anal. Bus. Ind. Towar. a Sustain. Econ. ICDABI 2020, 2020, doi:10.1109/ICDABI51230.2020.9325612.