We present the results of an experimental and computational pilot study of cranberry crop yield prediction using low-power microwave sensing and machine learning. We simulated backscattered radiation from cranberry canopies using plane-wave illumination with frequency content from 300 to 2400 MHz. The computational canopy domains allowed for variable soil moisture and cranberry yield in terms of cranberry mass per 1 ft <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of canopy surface area. We collected experimental field data with a prototype open-ended waveguide sensor operating between 600 and 1300 MHz. We measured experimental microwave signals by placing our sensor directly on top of cranberry-crop bed canopies in central Wisconsin and recording reflection coefficients across the operating band. We implemented a machine learning approach to map the microwave reflection coefficients to yield. The mapping procedure involves dimensionality reduction with principal component analysis, supervised learning with linear discriminant analysis, and error-correcting output codes. The idealized computational results demonstrate the feasibility of discriminating three cranberry volume fractions that are representative of central Wisconsin field conditions, at frequencies below 2 GHz. Performance evaluations of the machine learning algorithm applied to the measured field data indicated that, in 81% of test cases, the predicted crop yield had less than 8% error. Most importantly, the average yield prediction error was less than 1.3%. These pilot study results provide strong evidence that machine learning enables accurate cranberry yield estimation when trained with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> (field) microwave backscattered signals.