Smart agricultural company plants seeds for sustainable farming with Azure AI technology

Agro Brain

Agro-Brain began when two IT consultants wondered about the links between the most up-to-date agricultural science and the everyday farmer on the ground

When a Chinese farm owner wanted to set up sustainable practices that could scale to thousands of hectares of land, he turned to a creative tech startup for answers. That company, now TalentCloud, forged ahead with Microsoft Azure Machine Learning and Internet of Things (IoT) technology.

Since its 2012 founding, the company has parlayed those technologies into its Agro-Brain solution. Just as TalentCloud data scientists are more productive as they create the models that power the solution, Chinese farmers are also safely increasing their effectiveness, reducing pollution, and stepping up food safety.

Farmers and consumers have a new reason to be excited and reassured about the quality and safety of their food. While few shoppers may ever hear of Agro-Brain, the solution based on Microsoft Azure is a valuable tool for farmers. It provides full visibility into crop status and recommendations based on the latest agricultural science. That means less pollution and use of pesticides, higher quality and safety, and wins for everyone, from producer to consumer to the environment.

Agro-Brain began when two IT consultants wondered about the links between the most up-to-date agricultural science and the everyday farmer on the ground. Could it be that basing farming practice on data and insights could lead to wins for both farmers and consumers? And perhaps intelligent systems that could guide farmers and automate some farm functions would yield even greater benefits.

Convinced of the value of the idea, Xiaodong Wang (now Chief Executive Officer), and Guangliang Wei (Chief Technology Officer) formed TalentCloud. Resting on four pillars—data collection at the edge, plant science knowledge, farming best practices, and precision execution (circling back to the edge)—the solution combines a host of Azure technologies to promote sustainable agriculture and greater food safety.

Modernizing farming for people and the planet

Learning about traditional farming practices in China was an eye-opener for the founders of TalentCloud. “Lack of scientific knowledge is one of the biggest reasons for food safety crises and environmental pollution,” says Wang. “Traditionally, farmers have followed old ways without understanding the underlying science. China sends agricultural technicians to work with farmers, but there aren’t enough of them. And when in doubt, the farmers overuse chemicals.”

TalentCloud’s Agro-Brain solution helps farmers shift to data-centric practices. It tackles the problem on both the decision-making and operational levels. Agro-Brain uses cloud technology to gather millions of data points and synthesize them via sophisticated machine learning. It advises farmers and interacts with devices to control irrigation and other functions. Looking for a complete, end-to-end solution for Agro-Brain, TalentCloud chose Azure Machine Learning and Azure IoT services.

Harvesting food—and knowledge—from the fields

The Agro-Brain solution creates a closed-loop information system, combining real-time data collection in the field with plant science data that describes the entire life cycle of food crops, including related information about threats like pests, diseases, and growing conditions. The combination gives farmers access to up-to-date, customized crop management recommendations.

TalentCloud uses Microsoft products and services to optimize a four-part system that collects an array of data points—including soil conditions, air temperature, and humidity—from the field via IoT sensors. Camera images of crops and mobile device data supplement quantitative data. Sensors connect directly to Azure IoT Hub, which feeds data into Azure Machine Learning, rapidly training the models created by TalentCloud data scientists.

The automated machine learning capabilities in Azure Machine Learning accelerate model development, creating faster operational plans and forecasts for farmers. Other knowledge—for example, watering and fertilizer recommendations—returns back to the field as operational instructions, activating automated systems through Azure IoT Edge.

We use our own and third-party cookies to enable and improve your browsing experience on our website. If you go on surfing, we will consider you accepting its use.