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In the paper, we investigate using CPU temperature from small, low cost, single-board computers to predict out- door temperature in IoT-based precision agricultural settings. Temperature is a key metric in these settings that is used to in- form and actuate farm operations such as irrigation schedul- ing, frost damage mitigation, and greenhouse management. Using cheap single-board computers as temperature sensors can drive down the cost of sensing in these applications and make it possible to monitor a large number of micro-climates concurrently. We have developed a system in which devices communicate their CPU measurements to an on-farm edge cloud. The edge cloud uses a combination of calibration, smoothing (noise removal), and linear regression to make pre- dictions of the outdoor temperature at each device. We eval- uate the accuracy of this approach for different temperature sensors, devices, and locations, as well as different training and calibration durations.