Bayer Crop Science leverages image analytics for precision agriculture

Images captured by drones and processed by artificial intelligence help Bayer Crop Science better serve its customers and develop crops that are more resistant to insects, disease, and storm damage.

Using data has been an important element of Bayer Crop Science for decades, but the organization got a real data-driven shot in the arm with Bayer's 2018 acquisition of Monsanto. Since then, it has sought to apply machine learning and artificial intelligence to every aspect of its business.

One area where AI is playing an increasingly important role is precision agriculture. Traditional farming today means planting your field with seeds, spraying the entire field with fertilizer and pesticides, and irrigating the entire field. But fields aren't uniform: The soil isn't necessarily the same across the field, some parts need more fertilizer or less; some areas need more water or less; and so on. Precision agriculture seeks to bring more intelligence to the process.

"For precision planting and precision farming, companies like Bayer work really closely with the farmers to understand their land, their acreage, the type of soil they have, the water flow, and then work with them," says Michelle Lacy, data strategy lead for R&D in the Plant Biotechnology Division at Bayer Crop Science. "These are the best seeds; these are the best plant breeds that work well in this area of your farm. Planting the right seed type — the right corn line or soybean line or what have you — that will thrive in that soil type or the amount of water or nitrogen that you have. That's precision farming."

This data-driven approach to agriculture requires data to be collected by farmers and their equipment, such as combines that take soil measurements as they work; third parties, including academics and government employees; and Bayer Crop Science employees.

Bayer is now leveraging image analytics powered by TIBCO Spotfire visual analytics as part of that effort. It uses drones that take high-definition pictures to monitor crops. Plant height, color/greenness, and whether plants are diseased or infested with insects can be captured by images. A machine learning algorithm processes those images and blends the data with data about the soil, irrigation, and fertilization to provide in-depth insights into each part of a field. Bayer Crop Science uses TIBCO Data Virtualization software to understand the various data sources and schemas and bring them together without altering the physical source.

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