娇色导航

Farming is unpredictable, but FMC Corp. is using technology to help growers find more certainty.

FMC, an agricultural services company, has launched a mobile app to help growers of a variety of crops across the globe decide the best time to apply insecticides, fungicides, and other treatments and achieve the best results.

The company began working on its IT project around 2020, and by 2024, it rolled out the mobile app to 6,000 active users, covering more than 30 million acres. The app in 2024 covered 45 pests, including a variety of moths, boll weevils, and fall armyworms, for 28 crops across 25 countries. FMC plans to expand its use to 32 million acres, with 56 pests and 35 crops in 2025.

The app uses several data sources, including in-field IoT sensors, insect traps, historical weather data, and (GDD) forecasting, to create predictive insights on pest pressure on individual fields. FMC has created a GDD+ tool to forecast peak pest times based on unique, user-set biographical data, the company says.

Pest control is a major challenge for many farmers, and FMC’s goal is to give them more information about the best way to apply mitigation measures, says , the company’s director of precision agriculture.

The goal of the project is to support sustainable pest management and business scalability by optimizing the use of resources, reducing costs, and improving decision-making. The crop insights help farmers identify growth opportunities and plan expansions, FMC says.

The service covers large row crops such as wheat, corn, and cotton, but also vegetables and fruits like apples and strawberries. The app is available in many areas, including countries in Asia, Africa, and Latin America.

Putting mobile data to work worldwide

Depending on what part of the world they’re from, farmers tend to use the app in different ways, Sterling says. In the Asia Pacific region, farmers tend to rely on mobile data for much of their information, she notes.

“In the Philippines, Indonesia, and countries like that, you see a lot of small farmers who are really doing all of their decision-making from the mobile-first economy,” she says. “They’re doing all their decision-making on their phones, and they benefit from more advanced farming practices, where they may not know precisely when to apply yet.”

In countries with large agribusiness production, the use of the mobile app is often more about sustainability practices, with the app telling farms the optimal amounts of crop treatments to apply, Sterling adds.

FMC

“There’s the issue of, maybe you need to make sure you’re using products properly at the right time, so that you’re being a good steward of the product,” she says. “Especially in large row crops in places like Brazil, it’s really a lot about sustainability, ensuring that the products are being used at the right time.”

Arc, which earned the company a 2025 娇色导航100 Award for IT innovation and leadership, is one of a of mobile-based farming tools that bring new sets of data to growers, says , a Gartner analyst focused on advanced food production.

Projects like Arc may have limited value in the developed world, where new innovations can have incremental impacts due to widespread modern farming practices, but they could have huge benefits in the developing world, Ray says.

“In the US and Europe, farming is already highly mechanized, and it’s highly efficient,” he says. “But there are huge parts of the world that would benefit much more from this technology, and it really could be quite revolutionary.”

The key to these new mobile farming technologies is the availability of new data to the users, Ray adds. “The AI or the app is able to take advantage of information which may not be available for the farmer,” he says. “You think about something like crop pricing, predictions on crop pricing now that might make a difference on what you should be planting, but it’s not something that the farmer in standing in the field would necessarily know.”

Lots of data

For probabilistic forecast models, FMC has leveraged other datasets that can impact pest pressure, including weather, distance to roads and bodies of water, and distance between traps. The pest infestation can differ greatly between fields less than 10 miles apart, Sterling notes.

Using the collected information, the company has engineered dozens of complex variables to build state-of-the-art machine learning models to forecast the pest pressure within crop growing seasons.  The result is predictions with an accuracy confidence rate of more than 90%, FMC says.

Sterling believes digital apps are becoming the future of farming. “The fact that we have the natural world and the geographic world interacting with our products means that we have to get really innovative consistently with tech, and we need to make sure that our digital solutions are moving at the speed of our farmers’ needs,” she says.