Abstract
Food waste is one of the biggest costs of concern in all over the country. Research papers roughly say food wastage is about 1,300,000,000 tons. This paper provides a detailed view of predictive analytics of ML together comparing different strategies to separate different types of food. Non-destructive food quality is checked by based on the outer appearance of the given sample without destructing and sensed by the given sensors. This proposed system employed with PIC microcontroller which acts as a central processing unit, hygrometer, and temperature sensors together with three types of gas sensors which send the data sent to the cloud. For classifying the different types of foods like vegetables, fruits, and dairy qualities, the data is sensed with different types of grove gas sensors MQ9 (CO, Coal, Gas, Liquid Gas), MQ3 (Alcohol Vapor), and MQ2 (Combustible Gas, Smoke) furthermore with environmental sensors were acquired and sent that data to the Internet of Things. The novelty of this system is used to predict the quality of food under climatic conditions and also in traveling time with the help of IoT and their android application and the estimation of time that can preserve the different types of food in the storage.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pandian DAP (2019) Image inpainting technique for high quality and resolution enhanced image creation. J Innovative Image Process 1(1):39–50
Kumar TS, Senthil T (2019) A novel method for HDR video encoding, compression and quality evaluation. J Innovative Image Process (JIIP) 1(02):71–80
Gustavsson J, Cederberg C, Sonesson U (2020) Global food losses and food waste. In: Swedish institute for food and biotechnology (SIK) Gothenburg, Sweden, Accessed 31 March 2020
Wu Y et al. (2019) Air quality monitoring for vulnerable groups in residential environments using a multiple hazard gas detector. Sensors 19(2):362
Tiwari A, Sadistap S, Mahajan SK (2018) Development of an environment monitoring system using the internet of things. In: Ambient communications and computer systems, Springer, Singapore, pp 403–412
Hassan N et al. (2018) The role of edge computing in the internet of things. IEEE Commun Mag 56(11):110–115
Sharma SK, Wang X (2017) Live data analytics with collaborative edge and cloud processing in wireless IoT networks. IEEE Access 5:4621–4635
Chongtham Phisut P, Seesaard T, Kerdcharoen TV (2015) Monitoring of microbial canned food spoilage and contamination based on e-nose for smart home. In: 12th International conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTICON), IEEE
Kaur H, Sawhney BK, Jawandha SK (2018) Evaluation of plum fruit maturity by image processing techniques. J Food Sci Technol 55:3008–3015
Wijaya DR et al. (2017) Development of mobile electronic nose for beef quality monitoring. Proc Comput Sci 124:728–735
Alfian G et al. (2020) Improving efficiency of RFID-based traceability system for perishable food by utilizing IoT sensors and machine learning model. Food Control 110:107016
Ireri D, Belal E, Okinda C, Makange N, Ji C (2019) A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing. Artif Intell App 2:28–37
Zhang Z (2016) Missing data imputation: focusing on single imputation. Ann Transl Med 4(1)
Septiarini A, Hamdani H, Hatta HR, Kasim AA (2019) Image-based processing for ripeness classification of oil palm fruit. In: Proceedings of the 2019 5th international conference on science in information technology (ICSITech), Yogyakarta, Indonesia, pp. 23–26
Sahu B, Tiwari A, Raheja JL (2020) Development of machine learning and edge IoT based non-destructive food quality monitoring system using raspberry Pi 2020. In: IEEE International conference on computing, power and communication technologies (GUCON), Galgotias University, Greater Noida, UP, India
Nandi CS, Tudu B, Koley C (2016) A machine vision technique for grading Of harvested mangoes based on maturity and quality. IEEE Sens J 16:6387–6396
Géron A (2019) Hands-on machine learning with scikit-learn, keras, and tensorflow: concepts, tools, and techniques to build intelligent systems, O’Reilly Media
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shanthini, E., Sangeetha, V., Anusha, P.M., Jayanthi, A., Prakash, R.M., Prasanth, N.R. (2023). Non-destructive Food Quality Monitoring System. In: Bindhu, V., Tavares, J.M.R.S., Vuppalapati, C. (eds) Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems . Lecture Notes in Electrical Engineering, vol 977. Springer, Singapore. https://doi.org/10.1007/978-981-19-7753-4_80
Download citation
DOI: https://doi.org/10.1007/978-981-19-7753-4_80
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-7752-7
Online ISBN: 978-981-19-7753-4
eBook Packages: EngineeringEngineering (R0)