基于回归模型的作物产量预测：五种模型性能对比评估
Crop Yield Prediction Using Regression Models: A Comparative Evaluation of Model Performance
AI 导读
一项研究对比评估了五种回归模型，利用环境、作物和农业管理变量预测作物产量。数据集含 100 万条观测和 10 个变量，剔除 231 条负值目标后，在 80:20 划分下 LR 测试 R-squared 约 0.9130、RMSE 0.5008；70:30 划分下 lasso 回归测试 R-squared 最高约 0.9131。
正文 · 原文
Abstract:Reliable crop-yield prediction can support agricultural planning and resource allocation. This study evaluates five regression models for predicting crop yield from environmental, crop-related, and agricultural management variables. The dataset initially contained 1,000,000 observations and ten variables, with 231 observations having negative target values removed during preprocessing. Categorical variables were represented using one-hot encoding, and Boolean variables were converted into binary values. The models considered were multiple linear regression (LR), random-forest regression (RF), decision-tree regression (DT), ridge regression, and lasso regression. Their performance was evaluated using 80:20 and 70:30 training-testing splits based on the coefficient of determination (R-squared), mean squared error (MSE), and root mean squared error (RMSE). Applying an 80:20 split, LR achieved a test R-squared of approximately 0.9130 and a test RMSE of 0.5008. Applying a 70:30 split, lasso regression yielded the highest test R-squared (approximately 0.9131) and the lowest test RMSE (approximately 0.5008), with performance almost identical to LR and ridge regression. DT also showed improved test performance under the 70:30 split, whereas RF exhibited a substantial gap between training and testing performance. These results indicate that the evaluated linear regression model provides competitive baselines for this dataset. Further experimentation, including validation testing, will help determine the models' performance and predictability.
| Comments: | Experimental evaluation of crop-yield prediction using regression models |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.37022 [cs.LG] |
| (or arXiv:2610.37022v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37022 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Saptak Das Mr. [view email]
[v1]
Tue, 27 Oct 2026 10:58:16 UTC (11 KB)
来源：arXiv cs.AI · arxiv.org