Figure 1
From: An interaction regression model for crop yield prediction
Illustration of the proposed interaction regression model for crop yield prediction. Step 1 is data pre-processing. In step 2, Algorithms 1 and 2 select robust features and interactions, which are then used in step 3 to predict the crop yield with a multiple linear regression model. Here, \(\hat{y}\) is the predicted yield, \(\beta _\text {W}\), \(\beta _\text {S}\), and \(\beta _\text {M}\) are, respectively, the additive effects of weather, soil, and management features, whereas \(\beta _\text {I}\) is the effect of E × M interactions. This plot was created with Microsof PowerPoint (Version 16.0.12827.20200 32-bit).
