Environmental protection by minimizing food waste using AI driven food supply chain framework
DOI:
https://doi.org/10.55779/ng54461Keywords:
Artificial Intelligence, deep learning, food supply chain, food waste, internet of things, machine learning, sustainabilityAbstract
Food waste is a critical global issue, with an estimated 33% to 40% of food produced worldwide lost or wasted each year, while nearly 800 million people suffer from hunger. This dichotomy highlights the urgent need to enhance sustainability in the Food Supply Chain (FSC), where inefficiencies lead to economic losses and environmental degradation. Despite advancements in technology and management, food waste remains prevalent across the FSC- from production to retail, posing challenges to both sustainability and profitability. This study proposes an AI-driven framework to minimize food waste throughout the FSC, integrating machine learning (ML) models and Internet of Things (IoT) architecture. The proposed framework leverages IoT-enabled, real-time sensor data combined with Machine Learning (ML) models for crop yield prediction, demand forecasting, and shelf-life monitoring. At the production stage, MLP FI based model is employed for crop yield prediction that helps in reducing overproduction and shortages by providing accurate yield estimates. For demand forecasting, the PROPHET time series model was employed, which captures non-linear trends, seasonal patterns, and event-specific effects to maintain balanced inventory and minimize surplus. At the retail stage, the SqueezeViT-based shelf-life prediction model analyses visual characteristics of perishable goods to predict freshness accurately, allowing for timely interventions. Experimental results show the MLP-FI model achieved a Mean Absolute Percentage Error (MAPE) of 10.8% for crop yield prediction and the SqueezeViT model achieved 78.3% accuracy in shelf-life prediction, substantially outperforming baseline methods. This integrated AI-driven framework is estimated to reduce food waste by ~15% across the supply chain, demonstrating a scalable approach to optimize resource use and improve sustainability.
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References
Adamashvili N, Chiara F, Fiore M (2020). Food loss and waste, a global responsibility?!. Economia Agro-Alimentare 21(3):825-846. https://doi.org/10.3280/ecag2019-003014
Amentae TK, Gebresenbet G (2021). Digitalization and future agro-food supply chain management: A literature-based implications. Sustainability 13(21):12181. https://doi.org/10.3390/su132112181
Baswoju SP, Latha Y, Changala R, Gummadi A (2023). Development of CNN model to avoid food spoiling level. International Journal of Scientific Research in Computer Science, Engineering and Information Technology 9(5):261-268. https://doi.org/10.32628/CSEIT2390536
Bhatia S, Albarrak AS (2023). A blockchain-driven food supply chain management using QR code and XAI-faster RCNN architecture. Sustainability 15(3):2579. https://doi.org/10.3390/su15032579
Bongarde D, Pandit S, Pandit H (2024). Use of machine learning and artificial intelligence in food spoilage detection. International Journal of Engineering Research and Applications 14(4):79-85.
Capone R, Berjan S, El Bilali H, Debs P, Allahyari MS (2020). Environmental implications of global food loss and waste with a glimpse on the Mediterranean region. International Food Research Journal 27(6):988-1000.
Chauhan C, Dhir A, Akram MU, Salo J (2021). Food loss and waste in food supply chains. A systematic literature review and framework development approach. Journal of Cleaner Production 295:126438. https://doi.org/10.1016/j.jclepro.2021.126438
Chen H, Chen Z, Lin F, Zhuang P (2021). Effective management for blockchain-based agri-food supply chains using deep reinforcement learning. IEEE Access 9:36008-36018. https://doi.org/10.1109/access.2021.3062410
Choudhury A, Jones J (2014). Crop yield prediction using time series models. Journal of Economics and Economic Education Research 15(3):53-68.
Dey S, Saha S, Singh AK, McDonald-Maier K (2022). SmartNoshWaste: Using blockchain, machine learning, cloud computing and QR code to reduce food waste in decentralized Web 3.0 enabled smart cities. Smart Cities 5(1):162-176. https://doi.org/10.3390/smartcities5010011
Elavarasan D, Vincent PMD (2020). Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE Access 8:86886-86901. https://doi.org/10.1109/ACCESS.2020.2992480
FAO (2011). Global food losses and food waste - Extent, Causes and Prevention. Rome.
FAO (2013). The state of food insecurity in the world: the multiple dimensions of food security. Rome.
FAO (2014). Food wastage footprint: Full-cost accounting: Final report. Natural Resources Management and Environment Department.
FAO (2018). SDG indicator 12.3.1 – Global Food Loss Index.
Government of India (2023). Agricultural statistics at a glance 2022. Economics and Statistics Division, Department of Agriculture and Farmers Welfare, Ministry of Agriculture and Farmers Welfare.
Han J, Li T, He Y, Gao Q (2022). Using machine learning approaches for food quality detection. Mathematical Problems in Engineering 2022(1):6852022. https://doi.org/10.1155/2022/6852022
Heydari M (2024). Cultivating sustainable global food supply chains: A multifaceted approach to mitigating food loss and waste for climate resilience. Journal of Cleaner Production 442:141037. https://doi.org/10.1016/j.jclepro.2024.141037
Hübner N, Caspers J, Coroamă VC, Finkbeiner M (2024). Machine‐learning‐based demand forecasting against food waste: Life cycle environmental impacts and benefits of a bakery case study. Journal of Industrial Ecology 28(5):1117-1131. https://doi.org/10.1111/jiec.13528
Jain P, Chawla P, Masud M, Mahajan S, Pandit AK (2022). Automated identification algorithm using CNN for computer vision in smart refrigerators. Computers, Materials & Continua 71(2):3337-3353. https://doi.org/10.32604/cmc.2022.023053
Kim S, Nam J, Ko BC (2022). Facial expression recognition based on squeeze vision transformer. Sensors 22(10):3729. https://doi.org/10.3390/s22103729
Lemaire A, Limbourg S (2019). How can food loss and waste management achieve sustainable development goals?. Journal of Cleaner Production 234:1221-1234. https://doi.org/10.1016/j.jclepro.2019.06.226
Li T, Sun Y (2022). An intelligent food inventory monitoring system using machine learning and computer vision. 3rd International Conference on Data Science and Machine Learning (DSML 2022), 2022:145-155.
Lohnes JD (2019). The food bank fix: Hunger, capitalism and humanitarian reason. Graduate Theses, Dissertations, and Problem Reports 3935.
Muth MK, Birney C, Cuéllar A, Finn SM, Freeman M, Galloway JN, … Zoubek S (2019). A systems approach to assessing environmental and economic effects of food loss and waste interventions in the United States. Science of the Total Environment 685:1240-1254. https://doi.org/10.1016/j.scitotenv.2019.06.230
Nerkar PM, Shinde SS, Liyakat KKS, Desai S, Kazi SSL (2023). Monitoring fresh fruit and food using Iot and machine learning to improve food safety and quality. Tuijin Jishu/Journal of Propulsion Technology 44(3):2927-2931.
Olowosoke CB (2022). Indiscriminate food insecurity: Road transportation management contribution in low/middle income economy. International Journal of Research Publication and Reviews 3(6):4758-4766. https://doi.org/10.55248/gengpi.2022.3.6.52
Panda SK, Mohanty SN (2023). Time series forecasting and modeling of food demand supply chain based on regressors analysis. IEEE Access 11:42679-42700. https://doi.org/10.1109/ACCESS.2023.3266275
Petrunina IV, Gorbunova NA, Zakharov AN (2023). Assessment of causes and consequences of food and agricultural raw material loss and opportunities for its reduction. Theory and Practice of Meat Processing 8(1):51-61. https://doi.org/10.21323/2414-438X-2023-8-1-51-61
Rodrigues M, Miguéis V, Freitas S, Machado T (2024). Machine learning models for short-term demand forecasting in food catering services: A solution to reduce food waste. Journal of Cleaner Production 435:140265. https://doi.org/10.1016/j.jclepro.2023.140265
Salihoglu G, Salihoglu NK, Ucaroglu S, Banar M (2018). Food loss and waste management in Turkey. Bioresource Technology 248:88-99. https://doi.org/10.1016/j.biortech.2017.06.083
Sanciolo P, Rivera E, Navaratna D, Duke MC (2022). Food waste diversion from landfills: A cost–benefit analysis of existing technological solutions based on greenhouse gas emissions. Sustainability 14(11):6753. https://doi.org/10.3390/su14116753
Shafi U, Mumtaz R, Anwar Z, Ajmal MM, Khan MA, Mahmood Z, … Jhanzab HM (2023). Tackling food insecurity using remote sensing and machine learning-based crop yield prediction. IEEE Access 11:108640-108657. https://doi.org/10.1109/ACCESS.2023.3321020
Usha R, Selvan RS, Avula BR, Chandrakanth P (2023). Development of CNN model to avoid the food spoiling level. 2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) 1:1-7.
Vilariño MV, Franco C, Quarrington C (2017). Food loss and waste reduction as an integral part of a circular economy. Frontiers in Environmental Science 5:21. https://doi.org/10.3389/fenvs.2017.00021
Wieben E (2016). The post-2015 development agenda: how food loss and waste (FLW) reduction can contribute towards environmental sustainability and the achievement of the Sustainable Development Goals. Dresden Nexus Conference Working Paper Series (Working Paper DNC2015/01). Hiroshan Hettiarachchi. Dresden: United Nations University Institute for Integrated Management of Material Fluxes and of Resources (UNU-FLORES).
Wunderlich S, Martinez NM (2018). Conserving natural resources through food loss reduction: Production and consumption stages of the food supply chain. International Soil and Water Conservation Research 6(4):331-339. https://doi.org/10.1016/j.iswcr.2018.06.002
Xue L, Liu G, Parfitt J, Liu X, Van Herpen E, Stenmarck Å, … Cheng S (2017). Missing food, missing data? A critical review of global food losses and food waste data. Environmental Science & Technology 51(12):6618-6633. https://doi.org/10.1021/acs.est.7b00401
Yang F, Moayedi H, Mosavi A (2021). Predicting the degree of dissolved oxygen using three types of multi-layer perceptron-based artificial neural networks. Sustainability 13(17):9898. https://doi.org/10.3390/su13179898
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