Nourishing the Future: AI-Driven Optimization of Farm-to-Consumer Food Supply Chain for Enhanced Business Performance

  • Hassan Anwar Department of Food Science & Technology, MNS-University of Agriculture, Multan, Pakistan
  • Talha Anwar Independent Researcher, Multan, Pakistan
  • Gohar Mahmood Department of Commerce, Bahauddin Zakariya University, Multan, Pakistan
Keywords: Artificial Intelligence (AI), business performance, consumer, food supply chain management


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Efficiency and sustainability are upending the global food supply system. The current study examined the relationship between Artificial Intelligence (AI) and agriculture to optimize food supply chain from farm to consumer business model. The study examined how AI-driven solutions may boost efficiency, reduce waste, and promote environmental responsibility with an emphasis on sustainability. The food supply system faces resource depletion, climate change, and growing global food consumption. AI technologies, such as automation, data analytics, and machine learning may solve these issues. AI systems use real-time data, predictive analytics, and intelligent logistics to improve production, distribution, and consumption. This reduces food production and transportation of carbon emissions along with improving resource allocation. AI-powered precision agriculture helps the farmers to increase crop yields while lowering fertilizer and pesticide use along with supporting sustainable farming as well. IoT devices and sensor networks are helpful to improve livestock management and crop monitoring, enabling data-driven agriculture. The current study highlighted how AI ensures food quality and safety across supply chain. By identifying impurities, monitoring storage conditions, and forecasting shelf life, AI-powered quality control systems may decrease food wastage and ensure safe, high-quality goods. In conclusion, AI-agriculture integration is an innovative way to increase farm-to-consumer food supply chain efficiency and sustainability. AI technology may help the supply chain stakeholders to create resource-efficient, environmentally friendly food production that meets the needs of a growing population. The current study discussed food industry sustainability and AI uses in agriculture, as well as future possibilities and challenges.


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M. De Lillo and H. J. Ferguson, “Kent academic repository,” Eur. J. Soc. Psychol., vol. 40, no. 2, pp. 366–374, 2022.

K. Mahroof, A. Omar, and B. Kucukaltan, “Sustainable food supply chains: Overcoming key challenges through digital technologies,” Int. J. Product. Perform. Manag., vol. 71, no. 3, pp. 981–1003, Oct. 2022, doi:

J. Monteiro and J. Barata, “Artificial intelligence in extended agri-food supply chain: A short review based on bibliometric analysis,” Procedia Comput. Sci., vol. 192, pp. 3020–3029, 2021, doi:

P. A. Hennelly, J. S. Srai, G. Graham, and S. F. Wamba, “Rethinking supply chains in the age of digitalization,” Prod. Plan. Control, vol. 31, no. 2–3, pp. 93–95, 2020, doi:

A. Iftekhar, X. Cui, M. Hassan, and W. Afzal, “Application of blockchain and internet of things to ensure tamper-proof data availability for food safety,” J. Food Qual., vol. 2020, June 2020, doi:

H. Anwar, T. Anwar, and M. S. Murtaza, “Applications of electronic nose and machine learning models in vegetables quality assessment: A review,” in Proc. 2023 IEEE Int. Conf. Emerg. Trends Eng. Sci. Technol. ICES, Jan. 2023, doi:

D. Enériz, N. Medrano, and B. Calvo, “An FPGA-Based machine learning tool for in-situ food quality tracking using sensor fusion,” Biosensors, vol. 11, no. 10, Art. no. 366, Sep. 2021, doi:

T. Anwar and H. Anwar, “Beef quality assessment using AutoML,” Proc. 2021 Mohammad Ali Jinnah Univ. Int. Conf. Comput., MAJICC 2021, Jul. 2021, doi:

G. Zhao et al., “Blockchain technology in agri-food value chain management: A synthesis of applications, challenges and future research directions,” Comput. Ind., vol. 109, pp. 83–99, Aug. 2019,

Y. K. Dwivedi et al., “Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” Int. J. Inf. Manage, vol. 57, Art. no. 101994, Apr. 2021, doi:

P. Akhtar, S. Kaur, and K. Punjaisri, “Chain coordinators’ strategic leadership and coordination effectiveness: New Zealand-Euro agri-food supply chains,” Eur. Bus. Rev., vol. 29, no. 5, pp. 515–533, 2017, doi:

F. R. Lima-Junior and L. C. R. Carpinetti, “Quantitative models for supply chain performance evaluation: A literature review,” Comput. Ind. Eng., vol. 113, pp. 333–346, Sep. 2017, doi:

V. S. Kodogiannis, “Application of an electronic nose coupled with fuzzy-wavelet network for the detection of meat spoilage,” Food Bioproc. Tech., vol. 10, no. 4, pp. 730–749, Apr. 2017, doi: 016-1851-6

T. Anwar and H. Anwar, “LSNet: A novel CNN architecture to identify wrist fracture from a small X-ray dataset,” Int. J. Info. Technol., vol. 15, no. 5, pp. 2469–2477, June 2023, doi: 01311-W/TABLES/7

I. Chaudhary, H. Anwar, U. Latif, and A. Latif, “Role of artificial intelligence in different aspects of Public Health,” UMT Art. Intell. Rev, vol. 2, no. No 2, Dec. 2022, doi:

H. U. Khan et al., “Transforming the capabilities of artificial intelligence in gcc financial sector: A systematic literature review,” Wirel. Commun. Mob. Comput., vol. 2022, Art. no. 8725767, doi:

M. Mazzone and A. Elgammal, “Art, creativity, and the potential of artificial intelligence,” Arts, vol. 8, no. 1, Art. no. 26, Feb. 2019, doi:

I. H. Sarker, “AI-Based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems,” SN Comput. Sci., vol. 3, no. 2, pp. 1–20, Feb. 2022, doi: 01043-X

Y. Xu et al., “Artificial intelligence: A powerful paradigm for scientific research,” The Innov., vol. 2, no. 4, Art. no. 100179, Nov. 2021, doi: 0179

A. Almusaed, I. Yitmen, and A. Almssad, “Reviewing and integrating AEC practices into industry 6.0: Strategies for smart and sustainable future-built environments,” Sustainability, vol. 15, no. 18, Art. no. 13464, Sep. 2023, doi:

S. Chatterjee, R. Chaudhuri, D. Vrontis, and G. Giovando, “Digital workplace and organization performance: Moderating role of digital leadership capability,” J. Innov. Knowled., vol. 8, no. 1, Art. no. 100334, 2023, doi:

S. Makridakis, “The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms,” Futures, vol. 90, pp. 46–60, June 2017, doi: 7.03.006

G. Berti and C. Mulligan, “Competitiveness of small farms and innovative food supply chains: The role of food hubs in creating sustainable regional and local food systems,” Sustainability, vol. 8, no. 7, Art. no. 616, July 2016, doi:

A. Mittal, C. C. Krejci, and T. J. Craven, “Logistics best practices for regional food systems: A review,” Sustainability, vol. 10, no. 1, Art. no. 168, Jan. 2018, doi:

S. M. Schnell, “Food miles, local eating, and community supported agriculture: Putting local food in its place,” Agric. Hum. Val., vol. 30, no. 4, pp. 615–628, 2013, doi:

V. Borsellino, E. Schimmenti, and H. El Bilali, “Agri-Food markets towards sustainable patterns,” Sustainability, vol. 12, no. 6, Art. no. 2193, Mar. 2020, doi:

C. Feldmann and U. Hamm, “Consumers’ perceptions and preferences for local food: A review,” Food Qual. Prefer, vol. 40, pp. 152–164, Mar. 2015, doi:

Z. Nakat and C. Bou-Mitri, “COVID-19 and the food industry: Readiness assessment,” Food Cont., vol. 121, Art. no. 107661, Mar. 2021, doi:

M. Westerlund, S. Nene, S. Leminen, and M. Rajahonka, “An exploration of blockchain-based traceability in food supply chains: On the benefits of distributed digital records from farm to fork,” Tech. Innov. Manag. Rev., vol. 11, no. 6, pp. 6–19, June 2021, doi:

M. Lezoche, H. Panetto, J. Kacprzyk, J. E. Hernandez, and M. M. E. A. Díaz, “Agri-food 4.0: A survey of the Supply Chains and Technologies for the Future Agriculture,” Comput. Ind., vol. 117, Art. no. 103187, May 2020, doi:

D. Denkenberger, A. Sandberg, R. J. Tieman, and J. M. Pearce, “Long term cost-effectiveness of resilient foods for global catastrophes compared to artificial general intelligence safety,” Int. J. Disas. Risk Reduc., vol. 73, Art. no. 102798, Apr. 2022, doi:

D. J. McClements et al., “Building a resilient, sustainable, and healthier food supply through innovation and technology,” Ann. Rev. Food Sci. Technol., vol. 12, pp. 1–28, Mar. 2021, doi:

G. Rahmann et al., “Organic agriculture 3.0 is innovation with research,” Org. Agric., vol. 7, no. 3, pp. 169–197, Dec. 2016, doi:

T. Anwar and H. Anwar, “Citrus plant disease identification using deep learning with multiple transfer learning approaches,” Pak. J. Eng. Technol., vol. 3, no. 2, pp. 34–38, Apr. 2020, doi:

A. Iftekhar and X. Cui, “Blockchain-Based traceability system that ensures food safety measures to protect consumer safety and COVID-19 free supply chains,” Foods, vol. 10, no. 6, Art. no. 1289, June 2021, doi:

R. R. Mohamed, W. Hashim, T. M. Azahar, R. Yaakob, M. A. Mohamed, and K. A. A. Bakar, “Food freshness detection using smart machine learning classification,” J. Pharm. Neg. Results, vol. 13, pp. 7410–7426, Jan. 2023, doi: 9.868

H. Anwar, T. Anwar, and S. Murtaza, “Review on food quality assessment using machine learning and electronic nose system,” Biosens. Bioelec., vol. 14, Art. no. 100365, Sep. 2023, doi: 00365

R. Toorajipour, V. Sohrabpour, A. Nazarpour, P. Oghazi, and M. Fischl, “Artificial intelligence in supply chain management: A systematic literature review,” J. Bus. Res., vol. 122, pp. 502– 517, Jan. 2021, doi: 0.09.009

M. M. Mamoudan, A. Jafari, Z. Mohammadnazari, M. M. Nasiri, and M. Yazdani, “Hybrid machine learning-metaheuristic model for sustainable agri-food production and supply chain planning under water scarcity,” Resour. Environ. Sustainab., vol. 14, Art. no. 100133, Dec. 2023, doi: .100133

M. Harini, D. Dhinakaran, D. Prabhu, S. M. U. Sankar, V. Pooja, and P. K. Sruthi, “Levarging blockchain for transparency in agriculture supply chain management using iot and machine learning,” 2023 World Conf. Commun. Comput., July 2023, doi: 2023.10235156

M. Dora, A. Kumar, S. K. Mangla, A. Pant, and M. M. Kamal, “Critical success factors influencing artificial intelligence adoption in food supply chains,” Int. J. Produc. Res., vol. 60, no. 14, pp. 4621–4640, 2022, doi: 1959665

D. A. Donaldson, “Digital from farm to fork: Infrastructures of quality and control in food supply chains,” J. Rural. Stud., vol. 91, no. Sept. 2020, pp. 228–235, 2022, doi: 0.004

I. Santoso, M. Purnomo, A. A. Sulianto, and A. Choirun, “Machine learning application for sustainable agri-food supply chain performance: A review,” IOP Conf. Ser. Earth Environ. Sci., vol. 924, no. 1, 2021, doi: 1315/924/1/012059

P. Kittipanya-ngam and K. H. Tan, “A framework for food supply chain digitalization: lessons from Thailand,” Prod. Plan. Control, vol. 31, no. 2–3, pp. 158–172, 2020, doi: 1631462

P. W. Khan, Y. C. Byun, and N. Park, “IoT-Blockchain enabled optimized provenance system for food industry 4.0 using advanced deep learning,” Sensors, vol. 20, no. 10, Art. no. 2990, May 2020, doi:

G. Alfian, M. Syafrudin, N. L. Fitriyani, J. Rhee, M. R. Ma’arif, and I. Riadi, “Traceability system using IoT and forecasting model for food supply chain,” 2020 Int. Conf. Decision Aid Sci. Appl., Nov. 2020, pp. 903–907, doi: 20.9317011

S. K. Mangla, S. Luthra, N. Rich, D. Kumar, N. P. Rana, and Y. K. Dwivedi, “Enablers to implement sustainable initiatives in agri-food supply chains,” Int. J. Produc. Econ., vol. 203, pp. 379–393, Sep. 2018, doi:

G. Ji, L. Hu, and K. H. Tan, “A study on decision-making of food supply chain based on big data,” J. Syst. Sci. Syst. Eng., vol. 26, no. 2, pp. 183–198, Apr. 2017, doi:

D. Li and X. Wang, “Dynamic supply chain decisions based on networked sensor data: an application in the chilled food retail chain,” Int. J. Produc. Res., vol. 55, no. 17, pp. 5127–5141, Sep. 2017, doi:

S. K. Jagatheesaperumal, M. Rahouti, K. Ahmad, A. Al-Fuqaha, and M. Guizani, “The duo of artificial intelligence and big data for industry 4.0: Applications, techniques, challenges, and future research directions,” IEEE Inter. Things J., vol. 9, no. 15, pp. 12861–12885, Aug. 2022, doi:

S. Chowdhury et al., “Unlocking the value of artificial intelligence in human resource management through AI capability framework,” Hum. Resour. Manag. Rev., vol. 33, no. 1, Art. no. 100899, Mar. 2023, doi:

How to Cite
Anwar, H., Anwar, T., & Mahmood, G. (2023). Nourishing the Future: AI-Driven Optimization of Farm-to-Consumer Food Supply Chain for Enhanced Business Performance. Innovative Computing Review, 3(2).