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 (MLPC), histogram-based gradient
boosting classifier (HGBC), and a support vector machine (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.

应用机器学习算法预测 Russet 土豆克隆体在育种试验中的适应性，并通过使用来自 Oregon 州手动收集的试验数据，分析数据集的预处理、特征工程和缺失值插补等方面，论文结果强调了机器学习在土豆品种选择过程中的潜力和在决策中应用先进技术的相关性，为精确农业做出了贡献。