Building a Machine Learning Model to Predict Spring Wheat Crop Yield in Yuma, Arizona

Machine learning(ML) has been on the rise in many fields including agriculture. It is used for many things including crop yield prediction which is meant to help farmers decide when and what to grow based on the model. Many models have been built for various crops and areas of the world utilizing various sources of data. However, there is yet to exist a model designed to predict any crop’s yield in Yuma Arizona, one of the premier places to grow crops in America. For this, I built a dataset from farm documentation that describes the actions taken before, during, and after a crop is being grown. To supplement this data, ecological data was also used so data such as temperature, heat units, soil type, and soil water holding capacity were included. I used this dataset to train various regression models where I discovered that the farm data was useful, but only when used in conjunction with the ecological data.