Ensemble Learning for Improving Generalization in Aeroponics Yield Prediction

Abstract

Agriculture plays a crucial role in economy of several countries and yield prediction is essential for production management and operation planning. Machine Learning (ML) is a growing trend in determining yield as a complex function of multiple input variables. Aeroponics is one of the efficient sustainable farming methods and allows all season farming despite hostile outdoors growing environment. In this paper, yield prediction in aeroponics is studied using ML. We have compared and analyzed three popular supervised ML methods — Dense Neural Network (DNN), Random Forest based on decision trees (RF) and Support Vector Regression (SVR). Air quality and water quality measurements including temperature, humidity, CO 2 , pH and Total Dissolved Solids (TDS) are used for yield prediction. Other static inputs such as number of days before and after transplant are also used. Six crops are studied (garlic chives, basil, red chard, rainbow chard, arugula, and mint). DNN performs particularly well with the prediction. The root mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) are calculated to estimate the efficiency of the method. Mean square error and R2 score of DNN are 0.10 and 0.67, RF follows DNN correctness with MSE and R2 of 0.12 and 0.62, and SVR achieves 0.18 and 0.45 respectively, all of these values over the validation dataset. In addition to individual models, the two top 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.