Sensor Data Simulation and Predictive Modeling for Food Spoilage Risk in Green IoT Enabled Supply Chains
DOI:
https://doi.org/10.29284/f1x9z406Keywords:
Green IoT, Food Spoilage Prediction, Logistic Regression, Sensor-Based Risk Prediction, Deep LearningAbstract
One of the major causes of economic loss, greenhouse emissions, and environmental burden is spoilage of perishable food during the post-harvest and transportation stage. As food goes to waste before moving to the consumption points, the energy consumed in its production; water, fertilizers, harvesting fuel, cold storage electricity, packaging, and transport logistics are also wasted energy that increases monetary and ecological footprints. The systemic inefficiencies undermine the sustainability of the supply, drive food costs, and speed up the waste-related emissions, rendering cold-chain integrity a strategic requirement instead of an operation option in isolation. This paper builds a simulated green internet of things (IoT) cold-chain pipeline, which generates 500 sensor measurements of temperature, humidity, and vibration, as well as transport time. The mean-based imputation, Min-max scaling to multi-unit normalization and a joint use of Interquartile Range (IQR) and Z-score to treat outliers are used to control rigorous preprocessing in the pipeline to maintain distributional stability. Rationally planned modeling results demonstrate that temperature and transport duration are the most impactful factors on the risk of spoilage, which is supported through the spoilage kinetics theory, and that humidity and vibration are moderately affecting the risk variance, but are not the strongest predictors of risk variation. A baseline logistic regression classifier shows a high degree of discriminatory ability of the risk of spoilage. The results reveal cold-chain integrity and optimization of logistics as the most logical tools in waste reduction. The model also offers a platform of scalable further upgrades of non-linear machine learning in the future like the Random Forests and Deep Learning in addition to blockchain-based traceability to improve its transparency, sustainability and decentralized trust on Green internet of things (IoT)-enabled perishable food supply networks.
References
[1] FAO, The State of Food and Agriculture 2019. Rome, Italy: Food and Agriculture Organization, 2019.
[2] D. Li, X. Wang, and H. K. Chan, “Cold chain logistics and food safety: A review,” Trends in Food Science & Technology, vol. 100, pp. 11–20, 2020.
[3] S. Liu, Y. Xu, and J. Zhang, “Temperature effects on spoilage prediction models in perishable supply chains,” Journal of Food Engineering, vol. 263, pp. 12–20, 2019.
[4] C. Verdouw, S. Wolfert, A. Beulens, and A. Rialland, “Virtualization of food supply chains with the Internet of Things,” Journal of Food Engineering, vol. 176, pp. 128–136, 2016.
[5] M. A. Rahman, M. S. Hossain, and G. Fortino, “IoT-enabled food spoilage detection and prediction,” IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9770–9781, 2021.
[6] G. Kou, Y. Xu, Y. Peng, F. Shen, Y. Chen, and Y. Shi, “IoT-enabled traceability for cold chain logistics: A literature review,” Food Control, vol. 125, art. no. 107972, 2021.
[7] S. S. Kamble, A. Gunasekaran, and S. A. Gawankar, “Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications,” International Journal of Production Economics, vol. 219, pp. 179–194, 2020.
[8] Y. Zhang, L. Zhang, and X. Wang, “Machine learning approaches for food quality prediction: A systematic review,” Food Control, vol. 123, art. no. 107849, 2021.
[9] L. Zhou, C. Zhang, F. Liu, Z. Qiu, and Y. He, “Application of deep learning in food: A review,” Comprehensive Reviews in Food Science and Food Safety, vol. 19, no. 5, pp. 2166–2186, 2020.
[10] J. Parfitt, M. Barthel, and S. Macnaughton, “Food waste within food supply chains: Quantification and potential for change to 2050,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 365, no. 1554, pp. 3065–3081, 2010.
[11] F. Tian, “A supply chain traceability system for food safety based on HACCP, blockchain, and Internet of Things,” in Proc. 2017 Int. Conf. Service Systems and Service Management (ICSSSM), 2017, pp. 1–6.
[12] F. Casino, V. Kanakaris, T. K. Dasaklis, C. Patsakis, and C. Douligeris, “Blockchain-based food supply chain traceability: A case study in safety and quality,” Computers & Industrial Engineering, vol. 136, pp. 333–347, 2019.
[13] J. Wolfert, L. Ge, C. Verdouw, and M. J. Bogaardt, “Big data in smart farming: A review,” Agricultural Systems, vol. 153, pp. 69–80, 2017.
[14] E. O. Ojo, M. Raman, and Y. L. Chong, “Achieving sustainable performance in food supply chains through digital technologies adoption,” Sustainable Production and Consumption, vol. 27, pp. 1373–1387, 2021.
[15] M. M. Aung and Y. S. Chang, “Traceability in a food supply chain: Safety and quality perspectives,” Food Control, vol. 39, pp. 172–184, 2014.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
