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Food Monitoring Using Adaptive Naïve Bayes Prediction in IoT

  • Pramod D. GanjewarEmail author
  • Selvaraj Barani
  • Sanjeev J. Wagh
  • Santosh S. Sonavane
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

In real time, everything requires monitoring and controlling, especially in case of the protecting food from getting spoiled. In this paper, an Internet of Thing (IoT) based framework for food monitoring is proposed to protect food from getting spoiled due to changes in the environmental conditions during storage. In the existing scenario the prediction has been done based on the recorded sensed data and detailed analysis have been done to identify the factors affecting the food to get spoiled. Automated controlling mechanism is proposed in this work for controlling the environmental parameters with adaptive Naïve Byes prediction and IoT. In the proposed work, environmental parameters like temperature, humidity, moisture, light, etc., which affect on the quality of the nutritional values of food are considered which spoil the food, if they are not in the advisable range of their values. In this work online analysis would be done to predict the nutritional condition of the food to avoid the spoilage of the food. This will help to save food from getting spoiled and reduces the incidental losses in the business. All the sensed data will be stored on a cloud and the analysis would be performed for prediction of the environmental condition at the storage place to avoid food spoilage by changing to the suitable environmental condition, at the place. In the proposed work adaptive Naïve Bayes method is used for prediction of environmental condition at the place where food is stored and the harmful changes are monitored and action will be taken to provide advisable condition at the stored location.

Keywords

Internet of Things Framework Storage Analysis Nutritional values Cloud etc. 

References

  1. 1.
    Srivastava, A., Gulati, A.: iTrack: IoT framework for smart food monitoring system. Int. J. Comput. Appl. 148(12), 1–4 (2016)Google Scholar
  2. 2.
    Paliwal, K., Reddy, S.R.N.: Smart food tracker smartmonitoring system for food safety. In: National Conference on Product Design (NCPD 2016), July 2016Google Scholar
  3. 3.
    Sharma, V., Tiwari, R.: A review paper on IoT and its smart applications. Int. J. Sci. Eng. Technol. Res. (IJSETR) 5(2), 472–476 (2016)Google Scholar
  4. 4.
    Maksimovic, M., Vujovic, V., Omanovic-Miklicanin, E.: Application of Internet of Things in food packaging and transportation (2015). https://www.researchgate.net/publication/296486930CrossRefGoogle Scholar
  5. 5.
    Venkatesh, A., Saravanakumar, T., Vairamsrinivasan, S., Vigneshwar, A., Santhosh Kumar, M.: A food monitoring system based on bluetooth low energy and Internet of Things. Int. J. Eng. Res. Appl. 7(3), 30–34 (2017). www.ijera.comCrossRefGoogle Scholar
  6. 6.
    Sivakumar, D., Janaki, K.: Effective food preservation from atmospheric conditions between harvest to retail marketing using IoT. Int. J. Technol. Res. Innov. Solutions (IJTRIS) 1, 1–6 (2017)Google Scholar
  7. 7.
    Pler, S., Wolff, M., Fischer, W.-J.: Food intake monitoring: an acoustical approach to automated food intake activity detection and classification of consumed food. Fraunhofer Institute for Photonic Microsystems (IPMS), Dresden, Germany, Institute of Acoustics and Speech Communication (IAS), Dresden University of Technology, Dresden, Germany (2012)Google Scholar
  8. 8.
    Lim, D.-Y., Ryoo, Y.-J.: Development of remote monitoring system for cold-storage. In: 30th Annual Conference of IEEE Industrial Electronics Society, IECON 2004 (2004)Google Scholar
  9. 9.
    Meulebroeck, W., Thienpont, H., Ottevaere, H.: Photonics enhanced sensors for food monitoring: part 1. IEEE Instrum. Measur. Magaz. 19(6), 35–45 (2016)CrossRefGoogle Scholar
  10. 10.
    Wagle, R., Shah, M., Kadam, A., Sahu, R.: A survey on monitoring and control system for food storage using IoT. Int. J. Innov. Res. Comput. Commun. Eng. (An ISO 3297: 2007 Certified Organization) 5(5) (2017). www.ijircce.com
  11. 11.
    Ganjewar, P.D., Barani, S., Wagh, S.J.: Data reduction using incremental Naive Bayes Prediction (INBP) in WSN. In: 2015 International Conference on Information Processing (ICIP) (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Pramod D. Ganjewar
    • 1
    • 2
    Email author
  • Selvaraj Barani
    • 1
  • Sanjeev J. Wagh
    • 3
  • Santosh S. Sonavane
    • 4
  1. 1.Sathyabama Institute of Science and Technology (Deemed To Be University)ChennaiIndia
  2. 2.School of Computer Engineering and TechnologyMIT Academy of Engineering, Alandi(D.)PuneIndia
  3. 3.Government College of Engineering, KaradPuneIndia
  4. 4.DY Patil School of EngineeringPuneIndia

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