Land Suitability Assessment for Barley Yield Prediction Using Multicriteria Analysis

Reliable crop predictions are essential to make well-informed agricultural decisions. Most yield prediction methods are data intensive. Therefore, the purpose of this study was to develop yield prediction models based on remote and proximal sensing, which were assessed with simple linear regression (SLR), multiple linear regression (MLR), and random forest (RF). Among the different models, RF showed a significant improvement in the accuracy of barley yield prediction.