The goal of developing high-yield varieties in agriculture requires consistent improvement to meet / exceed the needs of client customers.
With the explosion of innovation in camera and drone development, it is now possible to readily capture and analyze these new sources of data. Enterprises seek access to these new data sources for a variety uses: predictive modeling of plant stressors, growth model alignment, the testing and efficacy of chemical application, variety performance, disease identification, prevention and treatment, and to perform new, large-scale data analytics.
The size and scale of these new data sources produce a tremendous amount of data for analysis. This wealth of data is necessary in many cases to provide statistically viable data sets on which to base business decisions.
The costs to capture, store and analyze this data often limits the scale of study, potentially inhibiting results.
Evaluating the cost-benefit analysis of how the data is stored, retained and analyzed is a key business driver for data intensive industries. The desired output for many organizations is to effectively minimize data storage and compute costs, while enabling evidence-based decisions to drive innovation.