Obstacles to developing cloud-based agronomic platforms when you have it all: FarmQA

October 11, 2018 | Case Study

Real-time cloud computed insight determined from generalizable machine learning algorithms with data fed from numerous perspectives (crop and environment) remains the pinnacle of digital agriculture platforms. Developers have pieces of the puzzle, but lack a comprehensive solution. Access to a variety of environmental monitoring solutions often impedes development. FarmQA possesses a comprehensive battery of environmental monitoring capabilities, but development of its cloud computing digital ag platform with machine learning capabilities has been slow. Lux spoke with FarmQA about its agronomic platform to learn more and identify the obstacles that slow development from the software perspective.  

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