Editorial Feature

From Farm to Fork: Cutting Down on Food Waste with Machine Learning

The 2021 IPCC report is unequivocal in its description of human-induced climate change and the damage this is causing to the planet. Food production is the leading contributor to climate change, causing land-use changes, carbon dioxide (CO2) emissions, and waste pollution at every stage from farm to fork. Despite this, we waste 1.3 billion tons of food globally every year. This article, part of a series examining the issues raised by the IPCC report, asks if machine learning can be used to reduce food waste.

How Does Food Waste Contribute to Climate Change?

Food production is the most significant cause of human-induced climate change, with 30% of all greenhouse gases in the atmosphere being caused by food production. Food production leads to more CO2 emissions than any country in the world, except for the USA and China.

Despite this, 30% of the food that is produced around the world is not even eaten. Worldwide, approximately 1.8 billion tons of food is wasted every year. This considerable quantity of food waste is responsible for an enormous portion of our total CO2 emissions: 8%.

In low-income countries, food is more likely to be wasted before it makes it to the consumer due to poor infrastructure. In high-income countries, on the other hand, consumers are to blame for more of the total food waste. In Canada, 47% of total food waste is wasted after being bought, 53% in Europe, and 70% in the UK.

Cutting Down on Food Waste with Machine Learning

Image Credit: SpeedKingZ/Shutterstock.com

How Can Machine Learning and AI Reduce Food Waste?

In middle- and high-income countries where consumers take more of the blame for food waste, greater adoption of Internet-of-Things (IoT) technologies can help to reduce food waste. Consumers with smart sensors in their fridges and pantry can automate their food purchases, set reminders or sensor-based alarms to let them know when food needs to be eaten, and save on food costs.

IoT assistance like this can be driven by machine learning to help consumers make the best possible decisions for themselves and their finances. However, the food industry itself may be the largest obstacle to widespread adoption. Consumer-driven food waste, which can be solved with behavioral changes, is ultimately profitable to the food industry.

However, food that is wasted before it reaches the end consumer presents a financial cost to the food industry and the environmental costs that all of us must pay for. Machine learning and algorithm approaches have been considered for food production and retail to reduce food waste.

Ocado: Machine Learning, AI, and Robotics in Food Retail

The UK delivery-only food retail service Ocado is a pioneer in grocery technology. The company has put a local IoT to work with machine learning algorithms and AI to reduce its rate of food waste to only 1 in every 6,000 items. Ocado’s end-to-end warehousing and delivery network guarantees fresh produce for every customer.

Forecasting and optimization are powered by machine learning in Ocado’s integrated eCommerce, logistics, and fulfillment platform. Algorithms determine what food customers need (and will need) and use this data to ensure no excess food is ordered from Ocado’s suppliers. AI is applied in the eCommerce store to predict demand for each of the products that Ocado stocks.

As well as minimizing waste and stock cover, machine learning also helps Ocado to increase efficiency in its automated warehouse operations. Proprietary robots work in mechanical grids in these warehouses and pick a 50-item order in only five minutes,  moving at 4 meters per second. This system is controlled by an AI with human supervision and results in even less food waste.

Simulation and predictive modeling ensure food products are correctly stored in the warehouse and delivery vans, reducing food waste in the Ocado system. Machine learning is also responsible for optimizing delivery routes in real-time throughout the day, leading to even less food waste and reduced CO2 emissions.

Inside A Warehouse Where Thousands Of Robots Pack Groceries

Video Credit: Tech Insider/YouTube.com

Reducing Food Waste in Production with Next-Generation Agricultural Robotics

Agricultural robotics is currently creating efficiency gains in agriculture that mean less energy and resources are required to produce food. Robots are used for weeding and harvesting, increasing precision and making the farming operation more efficient.

Iron Ox, based in California, designs and develops hydroponic greenhouses operated by robots. These greenhouses can produce food closer to large population centers, reducing the need for transportation and the CO2 emissions it causes.

A Cambridge University engineering team recently demonstrated a robot designed for harvesting lettuce. The robot was trained to recognize lettuce on a machine-learning algorithm. A computer vision system inputs data that the robot uses to find and harvest lettuces in the field.

Although this system was much slower and less efficient than human workers, the scientists believe that demonstrating the effectiveness of machine learning and robotics for food production applications will lead to more innovation in the future. They chose lettuce for this reason, as it is complicated to harvest with robots due to being close to the ground and relatively delicate.

Cutting Food Waste from Farm to Fork

All of these approaches to reducing food waste and increasing the efficiency of food production could, in the near future, be linked up in an IoT powered by machine learning and AI. Such a system would be optimized to ensure food is not produced unless it is likely to be used.

Connecting different machine-learning based systems, such as a home-sensor connected to an account with an advanced grocery retailer like Ocado, combined with more adoption of automation in food production, may significantly reduce food waste.

The technology currently exists for much of this to be presently viable. Considering the huge impact that food – and food waste – is having on the environment, machine learning should be adopted at much higher rates across food’s entire journey from farm to fork.

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Industrial Response to Climate Change 

This article is a part of the IPCC Editorial Series: Industrial Response to Climate Change, a collection of content exploring how different sectors are responding to issues highlighted within the IPCC 2018 and 2021 reports. Here, Robotics showcases the research institutions, industrial organizations, and innovative technologies driving adaptive solutions to mitigate climate change. 

References and Further Reading

IPCC. (2018) Summary for Policymakers. Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Available at: https://www.ipcc.ch/site/assets/uploads/sites/2/2019/05/SR15_SPM_version_report_LR.pdf

IPCC. (2021) Summary for Policymakers. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate. Available at: https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM.pdf

Belton, P. (2021) Ten Years Ago This Was Science Fiction’: The Rise of Weedkilling Robots. The Guardian. [Online] Available at: https://www.theguardian.com/environment/2021/aug/14/weedkilling-robots-farming-pesticide-use-sustainable

Birrell, S. et al. (2019) A Field Tested Robotic Harvesting System for Iceberg Lettuce. Journal of Field Robotics. [Online] Available at: https://doi.org/10.1002/rob.21888

Fearn, N. (2019) How Ocado Is Using Machine Learning To Reduce Food Waste And Feed The Hungry. Forbes. [Online] Available at: https://www.forbes.com/sites/nicholasfearn/2019/11/04/how-ocado-is-using-machine-learning-to-reduce-food-waste-and-feed-the-hungry/?sh=dd5efd61c1d6

Oakes, K. (2020). How Cutting Your Food Waste Can Help the Climate. BBC. [Online] Available at: https://www.bbc.com/future/article/20200224-how-cutting-your-food-waste-can-help-the-climate

Wrap (2021) Wasting Food Feeds Climate Change: Food Waste Action Week Launches to Help Tackle Climate Emergency. [Online] Available at: https://wrap.org.uk/media-centre/press-releases/wasting-food-feeds-climate-change-food-waste-action-week-launches-help

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Ben Pilkington

Written by

Ben Pilkington

Ben Pilkington is a freelance writer who is interested in society and technology. He enjoys learning how the latest scientific developments can affect us and imagining what will be possible in the future. Since completing graduate studies at Oxford University in 2016, Ben has reported on developments in computer software, the UK technology industry, digital rights and privacy, industrial automation, IoT, AI, additive manufacturing, sustainability, and clean technology.

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