Why is it that neighboring fields, with identical growing conditions, can produce such drastically different yields? This was the question that drove us to establish Prospera, and it’s what still drives us today.
Using our background in data science and machine learning, we explored ways to bring more certainty to an unpredictable industry. As the current agricultural revolution unfolds, digitalization will become the engine of crop production — to meet rising population demands and ensure environmental sustainability.
Taking a ground-up approach, our technology enables growers to make more informed, efficient, and scientific decisions. We make agriculture not only predictable, but optimizable across the entire growth cycle. Our technology advances agricultural productivity by helping growers do more with fewer resources.
From greenhouses to open fields, take a look at Prospera’s journey since our establishment in 2014.
Created the largest global, vertically-integrated artificial intelligence company in agriculture
Successful detection capability due to robust data sets and powerful ML algorithms
The launch of Valley Insights was so successful, we expanded its availability into 4 new states, quadrupling the coverage area.
Together, we aimed to transform the center pivot from an irrigation machine to an autonomous crop-management tool.
Applying our knowledge and application in greenhouses, we started focusing on open-fields.
Detected anomalies in greenhouses and provided irrigation management solutions.
Greenhouses provided consistent growth cycles to conduct our research methodologically, collect vast amounts of data and measure results.
We started our journey to understand why similar fields produce dramatically different yields.
Optimizing agriculture with data is extraordinarily complex. There are a large number of parameters to consider – their interactions vary and are hard to predict. To meet these challenges, we’ve developed some of the world’s most advanced, artificial intelligence technologies in-house.
We analyse field images to identify pests and diseases, monitor agro-technical activities and collect yield data. We use Deep Learning techniques to solve multi-dimensional planning and assignment optimization problems across massive data sets. And we extract insights from multiple data sources that are harmonized in an advanced Big Data framework.
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