It's the underlying science tools and heroes of the digital agriculture movement - specifically, machine learning and predictive modeling - that are central to the success of delivering a digital transformation.
Last year recorded groundbreaking technological advances in agriculture, even as the ag giants focused on consolidation. In looking back over the trends that shaped 2017, we can locate the signs pointing to the big stories we anticipate this year.
As the challenges to the agricultural industry continue to evolve, we must pay attention to the impact on farmers. We must support those who feed us by finding new ways to empower farmers, both professionally and personally.
Let's not forget that farmers themselves are very innovative and willing to try new things - still, they proceed with caution. So, technology adoption will most likely continue to be a pain point in agriculture, but having awareness of these principles may help developers smooth the path moving forward.
Blue River has designed and integrated computer vision and machine learning technology that will enable growers to reduce the use of herbicides by spraying only where weeds are present, optimizing the use of inputs in farming - a key objective of precision agriculture.
With Agrilyst, growers can understand which of their crops are the highest performers based on yield, growth rate, or both and run production scenarios with higher performers having more space allocation. These recommendations help growers optimize the space on their farm and drive significantly higher revenues.
Today, there are approximately 1 billion people who are underfed; and with an expected 9 billion people inhabiting the earth by 2050, we must use all of the technology that farmers have at their disposal. Technology is giving some promising solutions to this problem.
Precision agriculture isn't just rhetoric; it's real-time intelligence flowing into analytics software that transforms that flow into meaningful, practical information that farm managers can react to quickly. That data -- and that process -- have costs and, for the last few years, farmers have been stretched because commodity prices are down.
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