Measuring the Performance of Supervised Machine Learning Algorithms for Optimizing Wheat Productivity Prediction Models: A Comparative Study

Malik Muhammad Hussain, Farrukh Shehzad, Muhammad Islam, Ashique Ali Chohan, Rashid Ahmed, H. M. Muddasar Jamil Shera
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Abstract

The issue of precise crop prediction gained worldwide attention in the midst of food security concerns. In this study, the efficacies of different machine learning (ML) algorithms, i.e., multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), and support vector regression (SVR) are integrated to predict wheat productivity. The performances of ML algorithms are then measured to get the optimized model. The updated dataset is collected from the Crop Reporting Service for various agronomical constraints. Randomized data partitions, hyper-parametric tuning, complexity analysis, cross-validation measures, learning curves, evaluation metrics and prediction errors are used to get the optimized model. ML model is applied using 75% training dataset and 25% testing datasets. RFR achieved the highest R2 value of 0.90 for the training model, followed by DTR, MLR, and SVR. In the testing model, RFR also achieved an R2 value of 0.74, followed by MLR, DTR, and SVR. The lowest prediction error (P.E) is found for the RFR, followed by DTR, MLR, and SVR. K-Fold cross-validation measures also depict that RFR is an optimized model when compared with DTR, MLR and SVR.
衡量用于优化小麦生产力预测模型的监督机器学习算法的性能:比较研究
在粮食安全问题上,作物精确预测问题受到了全世界的关注。在本研究中,综合使用了不同的机器学习(ML)算法,即多元线性回归(MLR)、决策树回归(DTR)、随机森林回归(RFR)和支持向量回归(SVR)来预测小麦的生产力。然后测量 ML 算法的性能,以获得优化模型。更新的数据集是从作物报告服务中收集的各种农艺约束条件。使用随机数据分区、超参数调整、复杂性分析、交叉验证措施、学习曲线、评价指标和预测误差来获得优化模型。ML 模型使用 75% 的训练数据集和 25% 的测试数据集。在训练模型中,RFR 的 R2 值最高,达到 0.90,其次是 DTR、MLR 和 SVR。在测试模型中,RFR 的 R2 值也达到了 0.74,其次是 MLR、DTR 和 SVR。RFR 的预测误差(P.E)最小,其次是 DTR、MLR 和 SVR。K-Fold 交叉验证测量结果也表明,与 DTR、MLR 和 SVR 相比,RFR 是一个最佳模型。
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