基于FAO全球农业数据的机器学习作物产量预测:线性回归 随机森林 XGBoost比较研究
基于FAO全球数据比较三种机器学习模型预测作物产量，数据规模大，方法系统，结论可靠，对农业信息化研究有参考价值，但来源期刊权威性一般。
International Journal for Multidisciplinary Research (IJFMR) 2026年第8卷第4期(2026-08-28发表):基于FAO全球农业数据集(覆盖212个国家和地区、10种主要作物、1961-2016年56,717条记录),比较线性回归、随机森林、XGBoost三种机器学习模型。80:20 train-test split,Random Forest取得最佳性能:R²=0.9586、MAE=6724.19 hg/ha、RMSE=13829.60 hg/ha;XGBoost: R²=0.8115;线性回归:R²=0.0608。特征重要性分析识别作物类型(Item Code)为最具影响力预测因子,其次是地理区域和时间因素。[注:本论文使用2016年截止数据,模型泛化能力可能存在边界]
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| Author(s) | Abdulhafedh Mohammed Alsarfi, Archana Harsing Sable, Aymen Mosleh Al-Hejri |
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| Country | India |
| Abstract | Accurate crop yield prediction is essential for food security planning, agricultural policy-making, and sustainable farming management. This study develops and compares three machine learning models Linear Regression, Random Forest, and XGBoost for predicting crop yield using the FAO (Food and Agriculture Organization) global agricultural dataset. The dataset spans 212 countries and regions, covers 10 major crop types, and encompasses the period 1961–2016, comprising 56,717 records. Following a systematic preprocessing pipeline including missing value verification, duplicate removal, and label encoding, all models were trained and evaluated under identical experimental conditions using an 80:20 train-test split (45,373 training / 11,344 testing records). Performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). Random Forest achieved the best predictive performance with R² = 0.9586, MAE = 6,724.19 hg/ha, and RMSE = 13,829.60 hg/ha. XGBoost demonstrated competitive performance with R² = 0.8115, while Linear Regression showed limited effectiveness with R² = 0.0608, confirming its inability to model the nonlinear dynamics of global agricultural data. Feature importance analysis identified Item Code (crop type) as the most influential predictor, followed by geographic region and temporal factors. These findings confirm that ensemble learning methods, particularly Random Forest, are well-suited for modelling complex agricultural yield patterns at global scale. |
| Keywords | Crop yield prediction1; Machine learning2; Random Forest3; XGBoost4; Linear Regression5; FAO dataset6; Precision agriculture7; Feature importance8; Food security9. |
| Field | Computer |
| Published In | Volume 8, Issue 4, July-August 2026 |
| Published On | 2026-08-28 |
| DOI | https://doi.org/10.36948/ijfmr.2026.v08i04.86667 |
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