Sammy Selim strode through the dense, shiny green bushes on the slopes of his coffee farm in Sorwot village in Kericho, Kenya, accompanied by a younger farmer called Kennedy Kirui. They paused at each corner to input the farm's coordinates into a WhatsApp conversation. The conversation was with Virtual Agronomist, a tool that uses artificial intelligence to provide fertiliser application advice using chat prompts. The chatbot asked some further questions before producing a report saying that Selim should target a yield of 7.9 tonnes and use three types of fertiliser in specific quantities to achieve that goal. "My God!" Selim said upon receipt of the report.

Like humans, pigs have idiosyncratic faces, and new players in the Chinese pork market are taking notice, experimenting with increasingly sophisticated versions of facial recognition software for pigs. China is the world's largest exporter of pork, and is set to increase production next year by 9%. As the nation's pork farms grow in scale, more farmers are turning to AI systems like facial recognition technology – known as FRT – to continuously monitor, identify, and even feed their herds. This automated style of farming has the potential to be safer, cheaper and generally more effective: In 2018, pig farmers in China's Guangxi province trialling FRT found that it slashed costs, cut down on breeding time, and improved welfare outcomes for the pigs themselves. But it also has the potential to leave behind independent, small-scale farmers, who cannot afford to introduce this kind of technology to their operations.

Like humans, pigs have idiosyncratic faces, and new players in the Chinese pork market are taking notice, experimenting with increasingly sophisticated versions of facial recognition software for pigs. China is the world's largest exporter of pork, and is set to increase production next year by 9%. As the nation's pork farms grow in scale, more farmers are turning to AI systems like facial recognition technology – known as FRT – to continuously monitor, identify, and even feed their herds. This automated style of farming has the potential to be safer, cheaper and generally more effective: In 2018, pig farmers in China's Guangxi province trialling FRT found that it slashed costs, cut down on breeding time, and improved welfare outcomes for the pigs themselves. But it also has the potential to leave behind independent, small-scale farmers, who cannot afford to introduce this kind of technology to their operations.

During a workshop hosted at the International Conference on Learning Representations (ICLR) 2020, taking place on the web this week, panelists discussed how AI and machine learning might be -- and already has been -- applied to agricultural challenges. As several experts pointed out, countries around the world face a food supply shortfall -- an estimated 9% of the population (697 million people) are severely "food insecure," meaning they're without reliable access to affordable, nutritious food. Factors like labor shortages, the spread of pests and pathogens, and climate change threaten to escalate the crisis. IBM scientists spoke about their work in Africa with agricultural "digital twins," or digital models of crops used to forecast specific crop yields. And a team from the University of California, Davis detailed an effort to use satellite images to predict foraging conditions for livestock in Kenya.

Paris, October 02, 2019 – Capgemini has developed an intelligent data platform called Project FARM (Financial and Agricultural Recommendation Models), which is designed to optimize the agricultural value chain and bolster global food supply. The platform uses Artificial Intelligence (AI) to determine farming patterns through big data, generating insights from the data to make recommendations. It uses Machine Learning to make the platform applicable at scale by connecting it with cell phones. This solution has been built in collaboration with Agrics, a social enterprise operating in East Africa, which provides local farmers with agricultural products and services on credit. Global demand for food is anticipated to increase by 60% by 2050[1].

Global demand for food is anticipated to increase by 60% by 2050. Today, a great percentage of the world's population is fed by small-scale farmers, primarily from developing countries, using traditional methods and rudimentary farming practices. The complex value chain and the lack of resources and connectivity add to the agricultural inefficiency, so, there is a strong need for a wider package of yield optimizing and risk decreasing services for these small-scale farmers. Project FARM, created at Capgemini's Applied Innovation Exchange (AIE) Collaboration Zone (CoZone) in the Netherlands, aims to address these issues. The Project FARM platform uses Artificial Intelligence to determine farming patterns through big data, generating insights from the data to make recommendations.

Paris, October 02, 2019 – Capgemini has developed an intelligent data platform called Project FARM (Financial and Agricultural Recommendation Models), which is designed to optimise the agricultural value chain and bolster global food supply. The platform uses Artificial Intelligence (AI) to determine farming patterns through big data, generating insights from the data to make recommendations. It uses Machine Learning to make the platform applicable at scale by connecting it with cell phones. This solution has been built in collaboration with Agrics, a social enterprise operating in East Africa, which provides local farmers with agricultural products and services on credit. Global demand for food is anticipated to increase by 60% by 2050[1].

Why data could be the deciding factor in Africa's agricultural transformation. The world has a palm oil problem. It's a global, billion-dollar industry and its end result is irreversible environmental damage, ranging from deforestation and fires, to the loss of species such as tigers, pygmy elephants and orangutans. Palm oil is used in 50% of the products we buy (think bread, shampoo, soaps and even chocolate) due to the fact that it is the highest-yielding vegetable oil crop. Yet, in a country like Uganda, where 80% of the population is involved in agriculture as a way of life, many Ugandans farm oil palm on small plots, barely making a living. "The use of data for purposes of precision agricultural systems is being used around the world to optimize farms, from anticipating natural disasters such as droughts and flooding, to predicting the best time to harvest crops, to anticipating outbreaks of pests and disease before they impact the produce," says AgriSA's Janse Rabie.