Abstract: In order to predict plant stresses at a localized level, data feeds from many sensor types can be fused and analyzed to create a synthetic sensor estimating plant water stress, predicting microclimatic conditions, and performing localized plant disease and pest modeling. To make this affordable, an array of low-cost, lower precision sensors can be used. Sensor fusion is used to improve the accuracy of each sensing element by using machine learning to fuse data from the other sensing elements in the array. Additionally, machine learning can create a “synthetic sensor” replicating the output of high-cost and maintenance intensive sensing devices by using machine learning to replicate their output.