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Machine Learning: How A Game Of Checkers Is Shaping Agriculture

After playing game after game — weighing dozens of factors, calculating risk, and planning the next, most efficient moves — the computer “learned” to master the board.

Deep learning algorithms can take a decade of raw field data — like insights about how crops have performed in various climates, or how they have inherited certain characteristics — and use this data to develop a probability model.

Scientists are using computer simulations to conduct early tests to evaluate how a variety may perform when faced with different sub climates, soil types, weather patterns, and other factors.

'We were able to save an entire year of testing in our pipeline by using machine learning,” says Nalini Polavarapu, Enterprise Data Science Strategy Lead at Bayer, 'For a farmer, machine learning will help create personalized answers to 40 key decisions they make in a growing season — from planting to in-season management of irrigation, diseases, pests, and weeds to harvesting.'

Farmers can upload field images taken by satellites, UAVs, land based rovers, smartphones, and tools like the Climate FieldView™ platform, which can identify potential issues on the farm and recommend a management plan.

Bayer Brazil Shoot Crop disease is a major cause of famine and food insecurity around the world.3 Modern agriculture seeks to create seeds and crop protection products that can provide relief to these global challenges.

What we can imagine through machine learning has the potential to uncover even more ways to feed our growing world, adapt to climate change, and conserve our water, land, and energy.