Artificial intelligence can help achieve the goal of ‘food security’, i.e. the situation in which ‘all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life‘ (FAO, 1996). A brief scientific review to follow.
1) Hunger and ‘food insecurity’, introduction
The concepts of hunger and ‘food insecurity’ are often used as synonyms. More exactly:
– hunger is an unpleasant and/or painful physical experience caused by the inability to take in enough energy (kcal) and nutrients through food
– repeated inaccessibility to nutritious and safe food, in addition to hindering development and causing disease, expresses the risk of food insecurity.
The levels of severity of these phenomena are estimated through the Food Insecurity Experience Scale (FIES) developed by FAO (see Figure 1). An individual who goes without food for a day or more and goes without food is considered highly food insecure. (1)
Figure 1: Food Insecurity Experience Scale (adapted from FAO) (1)
2) ‘Food security’, objective missed
‘Food security’ is reached ‘when all people have physical and economic access to sufficiently safe and nutritious foods that meet their dietary needs and food preferences for an active and healthy life, regardless of their circumstances‘ (1996 World Food Summit).
The ‘food security’ objective defined in second place among the Sustainable Development Goals in the UN Agenda 2030 (#SDG2, Zero Hunger) is, however, destined to fail.
Food insecurity in fact, it records constant growth at a global level. Already in 2021, 11,7% of people on the planet suffered from extreme food insecurity, and the situation is getting worse. The latest SOFI report (State of Food Security and Nutrition in the World 2023), as seen, underestimates that at least 691-783 million people suffered from hunger in 2022. (2) This phenomenon is attributed to wars and the unequal distribution of resources between and within states, as well as the poor resilience of most of food systems and population growth.
3) Prospects for the use of artificial intelligence
Artificial intelligence (Artificial Intelligence, AI) applies scientific concepts, mathematical reasoning, statistics and probability, along with traditional scientific fields, to simulate human cognitive functions using computers. Its subsystems – Artificial Neural Networks (ANN), robotics, expert systems, computer vision, natural language processing and machine learning (ML) could significantly contribute to strengthening the four pillars of food safety: availability, use, stability and accessibility.
The applications of AI in agriculture they are indicated as potentially useful for increasing food production. (3) Its subsets have already been used to make decisions affecting various ecosystem processes throughout the food chain (4,5). Significant progress appears to have already been recorded in agricultural production, harvesting and marketing. AI can also be combined with IoT technologies (Internet of Things), in agri-food systems, to optimize the management of adverse conditions as well as that of waste and food waste (6,7).
4) Main challenges
The use of artificial intelligence in low and middle income countries (LMIC) it is influenced by various aspects. Economic, social, cultural, ethical and religious factors (8,9). The main challenges concern:
a) economic and financial charges (significant initial investments for hardware, software and sensors; unavailability of any local or international government funding program)
b) infrastructure (limited availability of electricity, lack of secure, high-speed Internet connections)
c) experts in the field (lack of advanced training, unavailability of experts in the field of agriculture and AI for food loss and waste)
d) availability of data (lack of centralized data, data scarcity and unavailability of historical data on key parameters such as soil conditions, crop growth, disease outbreaks, weather conditions, etc.)
e) customization (need to adapt AI models to regional variations, diversity and crop conditions)
f) regulatory framework (uncertainty of rules hinders the development of complex and constantly evolving technologies. With vulnerabilities on issues of ownership, privacy, copyright infringement and data exchange)
g) access to the market (inadequate transportation, inadequate storage facilities, lack of marketing knowledge, lack of e-commerce facilities in rural areas and above all lack of networking among small operators)
h) interdisciplinary collaboration (Bureaucracy in organizational structures can hinder collaborations, which tend to be lacking among professionals in data science, business, agronomy, engineering, politics, social sciences and artificial intelligence.)
5) Recommendations/efforts at international and EU level
The relevance and severity of food safety problems at a global level suggest greater commitment on the part of nations with more developed economies. With the aim of improving the efficiency of agri-food supply chains, the reduction of food losses and waste, access to the means of production and the fair redistribution of income and food. Also through the adoption of artificial intelligence. (10)
Papa Francesco had approved a renewed commitment by FAO, IBM, Microsoft and the Pontifical Academy for Life, to develop inclusive and AI forms aimed at promoting food and nutritional security. (11) The European Commission in turn is intensifying support for start-ups and SMEs (Small and Medium Enterprises), in order to develop reliable, resilient and strong AI technologies. (12) Also encouraging Member States to develop digital technologies for agriculture. (10)
6) AI, IoT and blockchain
Blockchain technology it also deserves consideration for the integrated management of reliable data in real time, to support artificial intelligence and IoT. In particular when data is collected from various resources, in agriculture (i.e. underground sensors, weather stations, drones, irrigation systems, related platforms) and throughout the supply chain (interactions between operators, and between them and control authorities ). (8)
An application of interest is being developed as part of the co-funded research project ‘Wasteless‘, in Horizon Europe. Where ours Wiise benefits is developing a blockchain technology aimed at collecting data on ‘food loss and waste’ in individual European agri-food supply chains, ‘from farm to fork’. With the dual objective of encouraging the adoption of good practices to improve circular economy performance and offering relevant statistical data. (13)
Srikanth Vuppala and Dario Dongo
Footnotes
(1) FAO. Hunger and Food Insecurity. https://www.fao.org/hunger/en/
(2) FAO, IFAD, UNICEF, W. and W. The State of Food Security and Nutrition in the World 2022. Repurposing Food and Agricultural Policies to Make Healthy Diets More Affordable. Rome, FAO. 2022
(3) Kutyauripo, I.; Rushambwa, M.; Chiwazi, L. Artificial Intelligence Applications in the Agrifood Sectors. J. Agric. Food Res. 2023, 11, 100502, doi:10.1016/j.jafr.2023.100502
(4) Pandey, D.K.; Mishra, R. Towards Sustainable Agriculture: Harnessing AI for Global Food Security. Artif. Intell. Agric. 2024, 12, 72–84, doi:10.1016/j.aiia.2024.04.003
(5) Sarku, R.; Clemen, U.A.; Clemen, T. The Application of Artificial Intelligence Models for Food Security: A Review. Agric.2023, 13, doi:10.3390/agriculture13102037
(6) Bilal, M.; Rubab, F.; Hussain, M.; Shah, SAR Agriculture Revolutionized by Artificial Intelligence: Harvesting the Future. 2024, 11, doi:10.3390/iocag2023-15875
(7) Chamara, R.M.S.R.; Senevirathne, SMP; Samarasinghe, SAILN; Premasiri, MWRC Sri Lanka Journal of Food and Agriculture (SLJFA). Role of Artificial Intelligence in Achieving Global Food Security: A Promising Technology for Future. 2020, 6, 43–70
(8) Ahmad, A.; Liew, A.X.W.; Venturini, F.; Kalogeras, A.; Candiani, A.; Di Benedetto, G.; Ajibola, S.; Cartujo, P.; Romero, P.; Lykoudi, A.; et al. AI Can Empower Agriculture for Global Food Security: Challenges and Prospects in Developing Nations. Front. Artif. Intell. 2024, 7, 1–18, doi:10.3389/frai.2024.1328530
(9) WHIG, P. Leveraging AI for Sustainable Agriculture: Opportunities and Challenges. Trans. Latest Trends Artif. Intell. Vol 4, No 4 Trans. Latest Trends Artif. Intell. 2023
(10) How, M.L.; Chan, Y.J.; Cheah, SM Predictive Insights for Improving the Resilience of Global Food Security Using Artificial Intelligence. Sustain. 2020, 12, doi:10.3390/SU12156272
(11) FAO. Artificial Intelligence Best-Practices in Agriculture Can Help Bridge the Digital Divide While Tackling Food Insecurity https://www.fao.org/newsroom/detail/Artificial-Intelligence-best-practices-in-agriculture-can-help-bridge-the-digital-divide-while-tackling-food-insecurity/en
(12) Directorate-General for Communication. More Support for Artificial Intelligence Start-Ups to Boost Innovation https://commission.europa.eu/news/more-support-artificial-intelligence-start-ups-boost-innovation-2024-01-24_en
(13) WASTELESS. Waste Quantification Solutions to Limit Environmental Stress. https://wastelesseu.com/
Srikanth Vuppala
Researcher, Ph.D. in Environmental Engineering, project manager in Wiise Srl benefit
Dario Dongo, lawyer and journalist, PhD in international food law, founder of WIISE (FARE - GIFT - Food Times) and Égalité.
