I have a dataset containing data on temperature, precipitation and soybean yields for a farm for 10 years (2005 - 2014). I would like to predict yields for 2015 based on this data.
Please note that the dataset has DAILY values for temperature and precipitation, but only 1 value per year for the yield, since harvesting of crop happens at end of growing season of crop.
I want to build a regression or some other machine learning based model to predict 2015 yields, based on a regression/some other model derived by studying the relation between yields and temperature and precipitation in previous years.
I am familiar with performing machine learning using scikit-learn. However, not sure how to represent this problem. The tricky part here is that temperature and precipitation are daily but yield is just 1 value per year.
How do I approach this?