Machine Learning Case Study

An agriculture research company, whose business is to perform farm management through the optimization of returns on inputs while preserving resources, reached out to Y&L to create a machine learning solution based upon farming stats that could assist them with predictive analysis.

The approach entailed collecting and integrating data from different data sources and then performing initial calculations which would then be fed into a model to predict the anticipated farm yield depending on varying factors such as elevation, temperature, soil condition and mean NDVI.

The result was a machine learning system which can provide farmers with intelligent, predictive insight on crop yield.

The technologies that were used included Openlayer, Geoserver, R language, OpenCPU, JavaScript and Scala.