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Blossom end rot disease tracking and prevention: a smart approach

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Abstract

Agriculture is the foundation of any nation. It is required for the comprehensive financial turn of events. The paper centers on Blossom End Rot infection which influences the tomato plant because of pH variety in the soil. This paper presents an investigation of different strategies to sift through disease, plant infection distinguishing proof utilizing layout coordinating picture order just as deep learning based Convolutional neural systems picture arrangement and proposes a framework to forestall the event of this ailment. Deep learning based disease identification has higher exactness than conventional strategies. In traditional methods, we just pH value of the soil is measured and remedial measure is taken, but here model is proposed that combines that disease detection at the early stage itself by using Deep Learning and IoT. The general goal is to improve rural profitability and homestead salary.

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References

  1. http://tnenvis.nic.in/Database/TN-ENVIS_792.aspx

  2. http://agro.unom.ac.in/wp-content/uploads/2015/09/State_Agri_profie_TamilNadu.pdf

  3. http://www.saferbrand.com/articles/common-tomato-plant-problems-how-to-fix-them

  4. Kamal M, Masazhar AN, Abdul R, Farah D (2018) Classification of leaf disease from image processing technique. Indonesian J Electr Eng Comput Sci 10(1):191–200. ISSN 2502-4752 E-ISSN 2502-4760

  5. Sun G, Jia X, Geng T (2018) Plant diseases recognition based on image processing technology. J Electr Comput Eng

  6. Singh V, Misra A (2015) Detection of unhealthy region of plant leaves using image processing and genetic algorithm. In: 2015 International conference on advances in computer engineering and applications, IEEE, pp 1028–1032

  7. Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318

    Article  Google Scholar 

  8. Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419

    Article  Google Scholar 

  9. Fuentes AF, Yoon S, Lee J, Park DS (2018) High-performance deep neural network-based tomato plant diseases and pests diagnosis system with refinement filter bank. Front Plant Sci

  10. Vigneshwar RK, Maheswari R (2016) Development of embedded based system to monitor elephant intrusion in forest border areas using internet of things. Int J Eng Res 5(7):594–598

    Google Scholar 

  11. Kumar N, Shimi SL (2017) Smart farming system for Indian farmers using Arduino based technology. Int J Adv Res Ideas InnovatTechnol 3(1):105–110

    Google Scholar 

  12. Vyas D, Borole A, Singh S (2016) Smart agriculture monitoring and data acquisition system. Int Res J EngTechnol (IRJET) 3(3):1823–1826

    Google Scholar 

  13. Gayatri MK, Jayasakthi J, Mala GA (2015) Providing smart agricultural solutions to farmers for better yielding using IoT. In: 2015 IEEE technological innovation in ICT for agriculture and rural development (TIAR), pp 40–43

  14. Nandurkar SR, Thool VR, Thool RC (2014) Design and development of precision agriculture system using wireless sensor network. In: 2014 First international conference on automation, control, energy and systems (ACES), pp 1–6, IEEE

  15. Rani MU, Kamalesh S (2014) Web based service to monitor automatic irrigation system for the agriculture field using sensors. In: 2014 International conference on advances in electrical engineering (ICAEE), pp 1–5

  16. Ali G, Shaikh AW, Shaikh ZA (2010) A framework for development of cost-effective irrigation control system based on Wireless Sensor and Actuator Network (WSAN) for efficient water management. In: 2010 2nd International conference on mechanical and electronics engineering, IEEE, vol 2, pp V2–378

  17. Gondchawar N, Kawitkar RS (2016) IoT based smart agriculture. Int J Adv Res ComputCommunEng 5(6):2278–1021

    Google Scholar 

  18. Shiraz Pasha BR, Yogesha DB (2014) Microcontroller based automated irrigation system. IJES 3(7):06–09

    Google Scholar 

  19. Barthiban SB, Chandra V, Jagadeesh R, MenagaDevi V, Saravanan S (2018) Smart agriculture monitoring and security system. Int J Trend Res Dev 5(2):97–99

    Google Scholar 

  20. Mojsovski F, Dimitrovski D (2018) Apple quality preservation with the use of intermittent drying process. J Environ Protect Ecol 19(4):1536–1543

    Google Scholar 

  21. GuYaqing, (2018) Evaluation of agricultural cultural heritage tourism resources based on grounded theory on example of Ancient TorreyaGrandis In Kuaiji Mountain. J Environ Protect Ecol 19(3):1193–1199

    Google Scholar 

  22. https://www.guru99.com/unsupervised-machine-learning.html#2

  23. https://towardsdatascience.com/machine-learning-algorithms-part-9-k-means-example-in-python-f2ad05ed5203

  24. https://www.analyticsvidentificationhya.com/blog/2019/01/build-image-classification-model-10-minutes/

  25. https://colab.research.google.com/github/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l05c01_dogs_vs_cats_without_augmentation.ipynb#scrollTo=KwQtSOz0VrVX

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Authors and Affiliations

  1. Department of ECE, Jansons Institute of Technology, Coimbatore, Tamilnadu, India

    Subramanian Saravanan, M. Nithyakumar, Vasagan Mana, S. Sangavi, S. N. Saran & A. M. Sowmya Lakshmi

  2. School of Computing, University of Eastern Finland, Kuopio, Finland

    Gao Xiao-Zhi

Authors
  1. Subramanian Saravanan
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  2. M. Nithyakumar
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  3. Vasagan Mana
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  4. S. Sangavi
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  5. S. N. Saran
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  6. A. M. Sowmya Lakshmi
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  7. Gao Xiao-Zhi
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Correspondence to Subramanian Saravanan.

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Saravanan, S., Nithyakumar, M., Mana, V. et al. Blossom end rot disease tracking and prevention: a smart approach. Int. j. inf. tecnol. 13, 801–806 (2021). https://doi.org/10.1007/s41870-021-00636-8

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  • DOI: https://doi.org/10.1007/s41870-021-00636-8

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