  We explore the application of machine learning algorithms to predict the
suitability of Russet potato clones for advancement in breeding trials.
Leveraging data from manually collected trials in the state of Oregon, we
investigate the potential of a wide variety of state-of-the-art binary
classification models. We conduct a comprehensive analysis of the dataset that
includes preprocessing, feature engineering, and imputation to address missing
values. We focus on several key metrics such as accuracy, F1-score, and
Matthews correlation coefficient (MCC) for model evaluation. The top-performing
models, namely the multi-layer perceptron classifier (MLPC), histogram-based
gradient boosting classifier (HGBC), and a support vector machine classifier
(SVC), demonstrate consistent and significant results. Variable selection
further enhances model performance and identifies influential features in
predicting trial outcomes. The findings emphasize the potential of machine
learning in streamlining the selection process for potato varieties, offering
benefits such as increased efficiency, substantial cost savings, and judicious
resource utilization. Our study contributes insights into precision agriculture
and showcases the relevance of advanced technologies for informed
decision-making in breeding programs.
