We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high‐yield, disease‐resistant, and climate‐resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state‐of‐the‐art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. 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 a feedforward neural network classifier (Neural Net), a histogram‐based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data‐generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data‐generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC‐ROC) and MCC. The simulation results highlight that non‐linear models like SVM and HGBC consistently show higher AUC‐ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. 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.

中文翻译：

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赤褐色马铃薯选择的预测分析


我们探索了机器学习算法的应用，特别是通过预测其适宜性来增强赤褐色马铃薯 （Solanum tuberosum L.） 克隆在育种试验中的选择过程。本研究解决了有效识别符合加工行业标准的高产、抗病和气候适应型马铃薯品种的挑战。利用从俄勒冈州试验中手动收集的数据，我们研究了各种最先进的二元分类模型的潜力。该数据集包括 1086 个克隆，每个克隆记录了 38 个属性的数据，重点关注产量、大小、外观和油炸特性，从 2013 年到 2021 年，几个对照品种在俄勒冈州的四个地区持续种植。我们对数据集进行全面分析，包括预处理、特征工程和插补以解决缺失值。我们专注于几个关键指标，例如准确性、F1 分数和 Matthews 相关系数 （MCC） 用于模型评估。性能最好的模型，即前馈神经网络分类器 （Neural Net）、基于直方图的梯度提升分类器 （HGBC） 和支持向量机分类器 （SVM），展示了一致且显著的结果。为了进一步验证我们的发现，我们使用目标、数据生成机制、估计量、方法和性能测量 （ADEMP） 框架进行了一项模拟研究，模拟不同的数据生成场景，以通过真阳性、真阴性、假阳性和假阴性分布、受试者工作特征曲线下面积 （AUC-ROC） 和 MCC 来评估模型的稳健性和性能。 仿真结果强调，像 SVM 和 HGBC 这样的非线性模型始终显示出比 Logistic 回归更高的 AUC-ROC 和 MCC，因此在各种分布中都优于传统的线性模型，并强调了模型选择和调整在农业试验中的重要性。变量选择进一步提高了模型性能，并确定了预测试验结果的有影响力的特征。研究结果强调了机器学习在简化马铃薯品种选择过程方面的潜力，提供了提高效率、大幅节省成本和明智地利用资源等好处。我们的研究为精准农业提供了见解，并展示了先进技术对育种计划中明智决策的相关性。