Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Improving a precision agriculture on-farm experimentation workflow through machine learning(Montana State University - Bozeman, College of Engineering, 2019) Peerlinck, Amy; Chairperson, Graduate Committee: John SheppardReducing environmental impact while simultaneously improving net return of crops is one of the key goals of Precision Agriculture (PA). To this end, an on-farm experimentation workflow was created that focuses on reducing the applied nitrogen (N) rate through variable rate application (VRA). The first step in the process, after gathering initial data from the farmers, creates experimental randomly stratified N prescription maps. One of the main concerns that arises for farmers within these maps is the large jumps in N rate between consecutive cells. To this end we successfully develop and apply a Genetic Algorithm to minimize rate jumps while maintaining stratification across yield and protein bins. The ultimate goal of the on-farm experiments is to determine the final N rate to be applied. This is accomplished by optimizing a net return function based on yield and protein prediction. Currently, these predictions are often done with simple linear and non-linear regression models. Our work introduces six different machine learning (ML) models for improving this task: a single layer feed-forward neural network (FFNN), a stacked auto-encoder (SAE), three different AdaBoost ensembles, and a bagging ensemble. The AdaBoost and bagging methods each use a single layer FFNN as its weak model. Furthermore, a simple spatial analysis is performed to create spatial data sets, to better represent the inherent spatial nature of the field data. These methods are applied to four actual fields' yield and protein data. The spatial data is shown to improve accuracy for most yield models. It does not perform as well on the protein data, possibly due to the small size of these data sets, resulting in a sparse data set and potential overfitting of the models. When comparing the predictive models, the deep network performed better than the shallow network, and the ensemble methods outperformed both the SAE and a single FFNN. Out of the four different ensemble methods, bagging had the most consistent performance across the yield and protein data sets. Overall, spatial bagging using FFNNs as the weak learner has the best performance for both yield and protein prediction.