Summary of Predictive Analytics Of Varieties Of Potatoes, by Fabiana Ferracina et al.
Predictive Analytics of Varieties of Potatoes
by Fabiana Ferracina, Bala Krishnamoorthy, Mahantesh Halappanavar, Shengwei Hu, Vidyasagar Sathuvalli
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study explores the application of machine learning algorithms to enhance the selection process of Russet potato clones in breeding trials by predicting their suitability for advancement. The researchers investigate the potential of state-of-the-art binary classification models using a dataset of 1086 clones with 38 attributes, including yield, size, appearance, and frying characteristics. They conduct preprocessing, feature engineering, and imputation to address missing values and evaluate the models’ performance using accuracy, F1-score, and Matthews correlation coefficient (MCC). The top-performing models include a neural network classifier, histogram-based gradient boosting classifier, and support vector machine classifier, which demonstrate consistent and significant results. To validate their findings, the researchers conduct a simulation study that assesses 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study aims to efficiently identify high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. By predicting the suitability of Russet potato clones for advancement, the researchers hope to improve the selection process in breeding trials. The study uses a dataset collected from trials in Oregon and applies machine learning algorithms, including neural networks, histogram-based gradient boosting classifiers, and support vector machine classifiers. These models are evaluated using metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC). The results show that non-linear models like support vector machines and histogram-based gradient boosting classifiers consistently outperform traditional linear models. |
Keywords
» Artificial intelligence » Auc » Boosting » Classification » F1 score » Feature engineering » Machine learning » Neural network » Support vector machine
