Agriculture plays a crucial role in economy and yield prediction is essential for production management and operation planning. In this paper, yield prediction in aeroponics is studied using Machine Learning. Air quality and water quality measurements are used for yield prediction, as well as other static variables. DNN performs particularly well with the prediction. Mean square error and R2 score of DNN are 0.10 and 0.67, over the validation dataset. Two well performing models are combined as an ensemble model to improve overall performance, which shows an average R2 score over the whole dataset divided by crop of 0.81.