Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review
Abstract
:1. Introduction
2. Current State of Food Waste and the Circular Economy
2.1. The Circular Economy Concept and Its Potential for Reducing Waste and Increasing Resource Efficiency
2.2. The Role of AI in Addressing Food Waste and Supporting the Circular Economy
2.3. Using AI to Support Circular Economy Initiatives
2.3.1. Use of AI to Identify Opportunities for Waste Reduction and Recycling
2.3.2. Applications of Artificial Intelligence (AI) in Waste Management and Recycling
2.3.3. Potential Benefits of an AI-Supported Circular Economy Initiative
3. Using AI to Monitor and Optimise Food Production and Supply Chains
3.1. Using AI to Analyse Data on Factors Such as Weather Patterns, Crop Yield, and Consumer Demand to Optimise Pre- and Post-Harvest Food Production and Supply Chains
3.2. Examples of AI Applications in Agriculture, Food Processing, and Transportation
3.3. Potential Benefits of AI Optimisation, including Reduced Food Waste and Increased Resource Efficiency
3.4. Examples of AI Applications in Food Production
- IBM Food Trust: This blockchain-based platform uses AI and other technologies to track food products from farm to table, enabling suppliers and retailers to identify the source of any safety or quality issues quickly. By providing end-to-end traceability, IBM Food Trust can help to reduce waste caused by recalls and increase consumer trust in the food supply chain [98].
- Blue River Technology: This company uses computer vision and machine learning algorithms to identify and selectively spray weeds in agricultural fields. By targeting only weeds, Blue River Technology can reduce the use of herbicides and increase crop yield, thus improving efficiency and sustainability in agriculture [99].
- Brightloom: This company uses AI and predictive analytics to optimise menu offerings and pricing for food retailers. By analysing data on sales and customer preferences, Brightloom can help retailers to reduce waste caused by overproduction and ensure that their offerings are aligned with customer demand [100].
- AgShift: This company uses computer vision and AI to automate the process of quality inspection for commodities such as grains, fruits, and vegetables. By analysing images and other data, AgShift can quickly and accurately identify defects, reducing waste caused by human error [101].
- ImpactVision: This company uses hyperspectral imaging and machine learning to analyse the composition of food products, enabling suppliers and retailers to ensure that their products meet quality standards. By identifying quality issues early, ImpactVision can help to reduce waste caused by recalls and improve overall efficiency in the supply chain [102].
4. AI-Powered Food Redistribution Systems
4.1. Using AI to Match Food Donors with Food Banks and Other Organisations That Distribute Food to People in Need
4.2. Examples of AI-Powered Food Redistribution Systems
4.3. Connection between GIS and AI
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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S/N | Technology | Application Examples | Role in Sustainability | References |
---|---|---|---|---|
1 | Machine Learning (ML) | ML can analyse consumer behaviour patterns to predict food purchases and reduce overproduction. | ML can help in sustainable food production by optimising crop yields based on weather patterns and soil conditions. | [42,43] |
2 | AI Image Recognition | Used in quality control for food items during manufacturing and packaging. Helps to minimise waste by identifying substandard products before reaching consumers. | AI image recognition can help design out food waste by ensuring only quality products are packaged and sold, reducing return rates and subsequent waste. | [44] |
3 | Natural Language Processing (NLP) | NLP can interpret the feedback provided by customers about food products and services to reduce food waste. | NLP can help in developing healthier food items by analysing customer feedback to identify demand for healthier options or improvements to existing items. | [45,46] |
4 | AI-Driven Smart Agriculture | AI applications can enhance farming methods, crop selection, and yield predictions, reducing the unnecessary waste of resources and promoting a circular economy. | AI can support local food production by optimising growing conditions for local species and forecasting market demand to reduce waste. | [47] |
5 | Internet of Things (IoT) and AI | IoT devices can collect data about food storage conditions, and AI can analyse these data to prevent spoilage, improving the shelf-life of food products. | IoT and AI can support the development of healthier food items by tracking nutritional value during storage and informing consumers. | [48] |
6 | Blockchain and AI | A combination of blockchain and AI can ensure traceability in the food supply chain, decreasing food waste and fraud. | Blockchain and AI can help design out food waste by ensuring transparency and accountability throughout the supply chain, reducing losses and inefficiencies. | [49,50] |
7 | Reinforcement Learning | AI systems can optimise food logistics and supply chain management, learning to improve over time and reduce food waste. | Reinforcement learning can support local food production by optimising delivery routes and times to ensure fresh, quality produce. | [51] |
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Onyeaka, H.; Tamasiga, P.; Nwauzoma, U.M.; Miri, T.; Juliet, U.C.; Nwaiwu, O.; Akinsemolu, A.A. Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review. Sustainability 2023, 15, 10482. https://doi.org/10.3390/su151310482
Onyeaka H, Tamasiga P, Nwauzoma UM, Miri T, Juliet UC, Nwaiwu O, Akinsemolu AA. Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review. Sustainability. 2023; 15(13):10482. https://doi.org/10.3390/su151310482
Chicago/Turabian StyleOnyeaka, Helen, Phemelo Tamasiga, Uju Mary Nwauzoma, Taghi Miri, Uche Chioma Juliet, Ogueri Nwaiwu, and Adenike A. Akinsemolu. 2023. "Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review" Sustainability 15, no. 13: 10482. https://doi.org/10.3390/su151310482