Datqorin / Canada
Use neural networks as a comparison, not a shortcut
A neural network can learn complex relationships, but the usefulness of a crop forecast still depends on data coverage, task definition and evaluation.
Build the baseline first
Compare with a simple approach using the same forecast horizon and held-out data. Complexity is useful only when its benefits survive an appropriate test.
Respect the data structure
Records from nearby fields or the same season may be closely related. Avoid a random split that makes the evaluation easier than the real forecasting problem.
Inspect where errors occur
Review results by crop, region and conditions represented in the data. Record limitations and do not assume performance transfers to a new growing context without evaluation.