This agricultural modeling website can help predict prices of agricultural commodities. For example, the price of wheat is determined by factors of supply and demand. One study found a strong correlation between these five factors and the price of wheat:
Using these as inputs, a linear regression model can be used to find the price of wheat per bushel.
The equation for this model uses the following variables:
The equation was created using a linear regression model and is given by:
Y = 8.463 - 0.109 A - 0.048 B - 0.401 C + 0.067 D - 0.529 E
Use this calculator to predict the price of wheat per bushel.
A:
B:
C:
D:
E:
Y = $
To improve the model, using artifical neural networks (ANNs) can learn the factors impacting the price beyond the linear model. The ANN had 3 layers with 32 nodes each. The activation function for each node was relu.
The ANN model performs much better than linear regression.
Use this ANN calculator to predict the price of wheat per bushel.
A:
B:
C:
D:
E:
Y = $
Another important source of food is corn. Here is the ANN model:
Use this ANN calculator to predict the price of corn per bushel.
A:
B:
C:
D:
E:
Y = $
Use this ANN calculator to predict the price of soybeans per bushel.
A:
B:
C:
D:
E:
Y = $
https://www.scirp.org/html/2-3001165_58614.htm#f1
https://www.macrotrends.net/2532/corn-prices-historical-chart-data
https://www.macrotrends.net/2531/soybean-prices-historical-chart-data