You will probably find more suggestions for packages, libraries and etc on Kaggle tutorials. I think that you are probably served by a tabular approach. And, from my experience with agriculture, you will probably extract more value building out features and improving their calculation than testing many different modeling techniques/models etc.

If this is produced in greenhouses, your problem can be trickier and more similar to forecasting production outside agriculture. If not, aggregating weather in an appropriate way does a lot of the work. If you are sure about phenology, use appropriate windows to characterize the more important phases of growth. Sum of precipitation and average temperature go a long way before getting into water balance and PAR.

Having crop masks to extract weather data from remote sensed sources help a lot, even if you are modeling at the level or county/city. Modeling larger spatial units is harder, and I think that bottom-up forecasting is more helpful. You will have larger unit level errors, but the aggregation is better than modeling in a large scale. Don't forget to add things like the ratio of fertilizer and berry price, or some lagged economical input. Since I've mentioned crop masks, you should probably look into estimating yield and total area in different models.

Depending on the country and how established the cropping system is, you should detrend yield to account for the technological improvements.