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Agrifood startups harness the power of artificial intelligence (AI)

Article-Agrifood startups harness the power of artificial intelligence (AI)

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From beekeeping to market making, two startups, BeeWise and Digital Green, are using technology and AI to improve the efficiency, profitability, and sustainability of global ingredient supply chains.

Advancements in technology such as artificial intelligence (AI), machine learning (ML) and deep learning have revolutionised the ways in which we produce, procure, and consume food and drink products in recent years. According to McKinsey, AI alone could increase productivity by up to 2% of annual revenues or generate an additional $660 billion for the consumer-packaged goods (CPG) sector.

Across the agrifood industry, a wide range of deep tech companies are emerging, promising to boost the efficiency of operations and accelerate the shift to sustainable food systems. From reviving declining global bee populations, to creating vast digital networks to enhance supply chain efficiency for small-holder farmers globally, these new technologies offer a wealth of benefits for food brands and the systems in which they operate.

During a panel discussion at the World Food Forum in Rome this October, several tech-enabled agrifood entrepreneurs came together to explore the potential of AI and other deep tech innovations to improve the offerings of food brands and drive forward the sustainable food system transition.

“AI has enabled us, for the first time in history, to delve into very complex problems and provide value to the food sector,” said Saar Safra, CEO and co-founder of Israeli ag-tech startup BeeWise.

Using technology to save dwindling honeybee populations

As the global population rises, the number of honeybee colonies per capita is decreasing at an alarming rate. According to data collected over more than six decades by the Food and Agriculture Organization (FAO), the number of colonies per capita globally shrunk by almost a quarter (24%) from 1961 to 2017. Likewise in the US, three-quarters of managed honeybee colonies were lost between 2020 and 2021.

Honeybees are vital to food production as they play a key role in crop pollination and produce marketable products that sustain communities such as honey, pollen, beeswax, and royal jelly. The increasing loss of bee populations threatens 75% of the world's food supply that depends on pollinators, according to a study published in the Environmental Health Perspectives journal.

Founded in 2018, BeeWise has created a robotic, automated beekeeping system to enable keepers to monitor, identify, and remotely tackle issues from their desktop or mobile device.

“There are around 50,000 bees in one box, as well as one queen. Analysing and understanding what the bees need is purely impossible without AI,” Safra said.

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AI helps to demystify complex processes in nature

The technology allows beekeepers to constantly observe what is happening in the hives and quickly remedy threats in the field in real time, without the need for human intervention.

“Nature is a complex system and AI gives us the tools to understand and provide value to it,” said Safra.

“It is impossible to solve this issue without the ability of AI to analyse the situation and give a coherent solution.”

Equipped with computer vision, AI, and precision robotics to allow for the digitisation of pollination and beekeeping, the startup’s solution, the BeeHome, has been reported to reduce colony loss by 70%, boost honey yield by 50%, and decrease labour by 90%.

“The data exists in nature and AI allows us to convert this [data] into information and actions and give people the opportunity to take a precise action at a precise time to create value,” Safra said.

Empowering farmers though integrated technology

Seeking to solve inefficiencies in food supply chains, particularly in rural communities, Indian startup Digital Green uses technology, AI, and data to empower, educate, and connect farmers across the world.

The company aims to provide farmers with knowledge and resources, enabling them to make informed decisions and adopt the most efficient and effective agricultural practices. The technology helps improve crop yields, livelihoods, and overall well-being for farmers, as well as improving the reliability, scope, and traceability of ingredient supply chains.

“Digital Green is using technology to extend and amplify the efficiency of agricultural practices,” said Rikin Gandhi, CEO of Digital Green.

Starting as a Microsoft Research spin-off based in India, Digital Green has since expanded its reach to three continents. To date, the startup has reached approximately 2.3 million households, making a significant impact on agriculture in these regions, it claims.

The business model is based on a three-pronged approach which involves an initial assessment and diagnosis of agricultural challenges and opportunities, leveraging insights and technology to enhance existing practises, and building communities via data analysis, evaluation, and knowledge sharing.

Cutting out the middleman between ingredient buyers and sellers

Digital Green uses videos as a tool to share best agricultural practices among its users and disseminate valuable information to rural communities, producing over 6,000 locally relevant videos in various languages to date. Through a mobile app, users can access real-time guidance and support on farming practices and can engage with a wider network of farmers globally.

“We have developed an app to engage with farmers and create videos to teach practices to others via a live chat … AI has allowed us to make videos in different languages and give instant feedback and advice to farmers,” Gandhi said.

The company’s free opensource software, Farm Stack, enables trusted data exchange between farmers themselves and between buyers and sellers across the value chain. The technology allows farmers to connect directly with ingredient retailers, increasing their market access and improving the efficiency and traceability of ingredient supply chains.

“The power of AI is in specificity … it’s important that communities are compensated for data they contribute to language models,” said Gandi.