Abstract
The expansion of the human population, climate change, the depletion of resources, and pollution all present obstacles to the functioning of our food systems, as well as to our capacity to ensure that future generations will have adequate food and nourishment. The existing methods used in agriculture and the supply chain are one of the primary factors that contribute to these problems. Artificial intelligence (AI) is currently permeating all aspects of food systems, and the methods in which it is doing so point to the possibility of transformative system changes. As intermediaries between people, technology, and the environment, designers have a responsibility to understand and reflect on the various ways in which artificial intelligence (AI) could bring about the change that is required to advance toward sustainable food systems. In the realms of commerce, corporate operations, and governmental policy, the application of artificial intelligence (AI) is quickly pushing the boundaries of what is possible. The intelligence of machines and robotics, empowered with the capacity for deep learning, is bringing about transformative and influential changes across all sectors of society, spanning from business to government. The food system and the various actors involved in it are a key contributor to climate change. They are also to blame for alterations in land use, the depletion of freshwater resources, and the degradation of aquatic and terrestrial ecosystems caused by excessive inputs of nitrogen and phosphorus. The applications of artificial intelligence (AI) are revolutionizing the agriculture industry and contributing to increased efficiency, sustainability, and productivity in farming practices. From using advanced technologies to monitor crop health and optimize interventions to automating various tasks and creating demand-driven supply chains, AI is making a significant impact on agriculture. It is also employed through applications that provide “augmented personalized health,” which, in turn, may better manage the food and nutrient intake of individuals with the purpose of producing healthier outcomes. There is a possibility that AI will revolutionize food systems, thereby facilitating a move toward reduced environmental consequences, higher resilience, and better health.
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References
Alexandratos N (1995) The outlook for World Food and Agriculture to the Year 2010. Islam 25–48
Ashton K (2018) How the term ‘internet of things’ was invented. Tech Republic
Bishop CM (2013) Model-based machine learning. Philos Trans R Soc A: Math Phys Eng Sci 371(1984):20120222
Capitanio F, Coppola A, Pascucci S (2010) Product and process innovation in the Italian food industry. Agribusiness 26(4):503–518
Cardello AV, Schutz HG, Lesher LL (2007) Consumer perceptions of foods processed by innovative and emerging technologies: a conjoint analytic study. Innov Food Sci Emerg Technol 8(1):73–83
Cheng JZ, Ni D, Chou YH et al (2016) Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep 6(1):24454
Fellows PJ (2022) Food processing technology: principles and practice. Woodhead publishing
Frohm J, Lindström V, Winroth M et al (2008) Levels of automation in manufacturing. Ergonomia-Int J Ergon Hum Factors 30:19
Godfray HCJ, Beddington JR, Crute IR et al (2010) Food security: the challenge of feeding 9 billion people. Science 327(5967):812–818
Goldberg Y (2016) A primer on neural network models for natural language processing. J Artif Intell Res 57:345–420
Greenspan H, Van Ginneken B, Summers RM (2016) Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imag 35(5):1153–1159
Gulshan V, Peng L, Coram M et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410
Habicht JP, Pelto G, Frongillo E et al (2004) Conceptualization and instrumentation of food insecurity. In: Workshop on the measurement of food insecurity and hunger, vol 15
Hermann KM, Kocisky T, Grefenstette E et al (2015) Teaching machines to read and comprehend. In: Advances in neural information processing systems, p 28
Hornberg A (2017) Handbook of machine and computer vision: the guide for developers and users. Wiley
Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255–260
Kakani V, Nguyen VH, Kumar BP et al (2020) A critical review on computer vision and artificial intelligence in food industry. J Agri Food Res 2:100033
Keeble M, Adams J, Sacks G et al (2020) Use of online food delivery services to order food prepared away-from-home and associated sociodemographic characteristics: a cross-sectional, multi-country analysis. Int J Environ Res Public Health 17(14):5190
Kurilyak S (2019) Artificial intelligence (AI) in food industry
Lasi H, Fettke P, Kemper HG et al (2014) Industry 4.0. Bus Inf Syst Eng 6(4):239–242
Leung XY, Wen H (2020) Chatbot usage in restaurant takeout orders: a comparison study of three ordering methods. J Hosp Tour Manag 45:377–386
Linko S (1998) Expert systems—What can they do for the food industry? Trends Food Sci Technol 9(1):3–12
MacLeod C (2002) Inventing the industrial revolution: the English patent system. Cambridge University Press, pp 1660–1800
Melander B, Lattanzi B, Pannacci E (2015) Intelligent versus non-intelligent mechanical intra-row weed control in transplanted onion and cabbage. Crop Prot 72:1–8
Misra NN, Dixit Y, Al-Mallahi A et al (2020) IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet Things J 9(9):6305–6324
Mnih V, Kavukcuoglu K, Silver D et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533
Mohd Khairi MT, Ibrahim S, Md Yunus MA et al (2018) Non-invasive techniques for detection of foreign bodies in food: a review. J Food Process Eng 41(6):e12808
Naik S, Patel B (2017) Thermal imaging with fuzzy classifier for maturity and size based non-destructive mango (Mangifera Indica L.) grading. ICE 15–20
Norvig PR, Intelligence SA (2002) A modern approach. Prentice Hall Upper Saddle River NJ, USA; Rani M, Nayak R, Vyas OP (2015) An ontology-based adaptive personalized e-learning system, assisted by software agents on cloud storage. Knowl-Based Syst 90:33–48
Plan S (2016) The national artificial intelligence research and development strategic plan. National Science and Technology Council, Networking and Information Technology Research and Development Subcommittee
Prince SJ (2012) Computer vision: models, learning and inference. Cambridge University Press
Rensi E (2018) McDonald’s says goodbye cashiers, hello kiosks
Ruckelshausen A, Biber P, Dorna M et al (2009) BoniRob–an autonomous field robot platform for individual plant phenotyping. Precis Agric 9(841):1
Sennaar K (2018) Examples of AI in restaurants and food services
Sebastin J (2018) Artificial intelligence: a real opportunity in food industry. Food Quality and Safety
Sanjana Rao GP, Aditya Shastry K, Sathyashree SR et al (2021) Machine learning based restaurant revenue prediction. In: Evolutionary computing and mobile sustainable networks: proceedings of ICECMSN 2020. Springer, Singapore, pp 363–371
Sezgin M, Sankur BL (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–168
Shaaban KM, Omar NM (2009) Region-based deformable net for automatic color image segmentation. Image vis Comput 27(10):1504–1514
Sonka M, Hlavac V, Boyle R (2014) Image processing, analysis, and machine vision. Cengage Learning
Soon JM, Brazier AK, Wallace CA (2020) Determining common contributory factors in food safety incidents—A review of global outbreaks and recalls 2008–2018. Trends Food Sci Technol 97:76–87
Van Der Maaten L, Postma E, Van den Herik J (2009) Dimensionality reduction: a comparative. J Mach Learn Res 10:66–71
Valous NA, Sun DW (2012) Image processing techniques for computer vision in the food and beverage industries. In: Computer vision technology in the food and beverage industries. Woodhead Publishing, pp 97–129
Völter M, Stahl T, Bettin J et al (2013) Model-driven software development: technology, engineering, management. Wiley
Wettels N, Santos VJ, Johansson RS et al (2008) Biomimetic tactile sensor array. Adv Robot 22(8):829–849
World Health Organization (2003) Assuring food safety and quality: guidelines for strengthening national food control systems. In: Assuring food safety and quality: guidelines for strengthening national food control systems, pp 73–73
Xiong Z, Sun DW, Pu H et al (2015) Non-destructive prediction of thiobarbituric acid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chem 179:175–181
Zhu L, Spachos P, Pensini E et al (2021) Deep learning and machine vision for food processing: a survey. CRFS 4:233–249
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Kaur, K., Priyanka, Kaur, G., Singh, B., Sehgal, S., Trehan, S. (2024). Artificial Intelligence (AI) as a Transitional Tool for Sustainable Food Systems. In: Thakur, M. (eds) Sustainable Food Systems (Volume II). World Sustainability Series. Springer, Cham. https://doi.org/10.1007/978-3-031-46046-3_15
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