Skip to main content

Wirelessly Controlled Plant Health Monitoring and Medicate System Based on IoT Technology

  • 304 Accesses

Part of the Communications in Computer and Information Science book series (CCIS,volume 1534)

Abstract

The availability of good quality fruits and vegetables is paramount in preventing starvation and minimizing outbreaks of diseases which leads to improving quality of life. One of the major obstacles of the mentioned availability is plant leaf disease. Although manpower plays a vital role in detecting such problems it is time-intensive, expensive, and very inefficient. Thus, developing a mechanism to vigorously monitor leaf's health and detect diseases of plant leaves at early stages is mandatory so that one can produce plenty. In this contribution, a system that detects leaf disease is developed using image processing algorithms, the k-nearest neighbor (KNN), support vector machine (SVM), and multilayer perception (MLP) machine learning algorithms are compared based on plant disease detection and classification systems performances. We also developed a prototype of simple-to-install technology that can recognize leaf diseases and allow medicine flow based on the results. This paper presents a smart plant health monitoring system that takes into account humidity, temperature, and soil contents.

Keywords

  • Image processing
  • IoT
  • KNN
  • MLP
  • SVM

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-96040-7_1
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-96040-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   149.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

References

  1. Pooja, V., Rahul, D., Kanchana, V.: Identification of plant leaf diseases using image processing techniques. In: 2017 IEEE International Conference on Technological Innovations in ICT for Agriculture and Rural Development (TIAR 2017). 978-1-5090-4437-5/17

    Google Scholar 

  2. Meena Prakash, R., Saraswathy, G.P., Ramalakshmi, G.: Detection of leaf diseases and classification using digital image processing. In: 2017 IEEE International Conference on Innovations in Information, Embedded, and Communication Systems (ICIIECS) (2017)

    Google Scholar 

  3. Tichkule, S.K., Dhanashri. H.: Plant diseases detection using image processing techniques. In: 2016 IEEE Online International Conference on Green Engineering and Technologies (IC-GET). 978-1-5090-4556-3/16

    Google Scholar 

  4. Suma, V., Amog Shetty, R., Rishab, F.T., Sunku, R. Triveni, S.P.: CNN-based leaf disease identification and remedy recommendation system. In: 2019 IEEE Proceedings of the Third International Conference on Electronics Communication and Aerospace Technology [ICECA 2019], pp. 395–399 (2019). 978-1–7281-0167-5/19

    Google Scholar 

  5. Devaraj, A., Rathan, K., Jaahnavi, S., Indira, K.: Identification of plant disease using image processing technique. In: 2019 IEEE International Conference on Communication and Signal Processing, April 4–6, 2019, pp. 749–753 (2019). 978-1-5386-7595-3/19

    Google Scholar 

  6. Channamallikarjuna, M., Edemialem, G., Fasil, E., Adugn, N.: Real-time automation of agriculture land, by automatically detecting plant leaf diseases and auto medicine. In: 32nd 2018 IEEE International Conference on Advanced Information Networking and Applications Workshops, pp. 325–330 (2018)

    Google Scholar 

  7. Saradhambal, G., Dhivya, R., Latha, S., Rajesh, R.: Plant disease detection and its solution using image classification. Int. J. Pure Appl. Math. 119(14), 879–884 (2018)

    Google Scholar 

  8. Gurleen, K., Rajbir, K.: Plant disease detection techniques: a review. In: 2019 IEEE International Conference on Automation, Computational and Technology Management (ICACTM), pp. 34–38 (2019). 978-1-5386-8010-0/19

    Google Scholar 

  9. Hingoliwala, M.B.H.A.: Smart farming: pomegranate disease detection using image processing. In: 2nd International Symposium on Computer Vision and the Internet, Vol. 58, pp. 280–288 (2015)

    Google Scholar 

  10. Vijai, S., Varsha, Misra, A.K.: Detection of an unhealthy region of plant leaves using image processing and genetic algorithm. In: 2015 IEEE International Conference on Advances in Computer Engineering and Applications, pp. 1028–1032 (2015). 978-1-4673-6911-4/15

    Google Scholar 

  11. Khirade, S.D., Patil, A.B.: Plant disease detection using image processing. In: 2015 IEEE International Conference on Computing Communication Control and Automation, pp. 768–771 (2018)

    Google Scholar 

  12. Chouhan, S.S., Kaul, A., Singh, U.P., Jain, S.: A database of leaf images: practice towards plant conservation with plant pathology. In: 2019 4th International Conference on Information Systems and Computer Networks (ISCON), pp. 700–707 (2019). https://doi.org/10.1109/ISCON47742.2019.9036158

  13. Rastogi, A., Arora, R., Sharma, S.: Leaf disease detection and grading using computer vision technology &fuzzy logic. In: 2015 IEEE 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 500–505 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Ayalew, L.G., Mattihalli, C., Asmare, F.M. (2022). Wirelessly Controlled Plant Health Monitoring and Medicate System Based on IoT Technology. In: Woungang, I., Dhurandher, S.K., Pattanaik, K.K., Verma, A., Verma, P. (eds) Advanced Network Technologies and Intelligent Computing. ANTIC 2021. Communications in Computer and Information Science, vol 1534. Springer, Cham. https://doi.org/10.1007/978-3-030-96040-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-96040-7_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96039-1

  • Online ISBN: 978-3-030-96040-7

  • eBook Packages: Computer ScienceComputer Science (R0)