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


  • Malik Muhammad Hussain Department of Statistics, Emerson University Multan, Pakistan
  • Farrukh Shehzad Department of Statistics, the Islamia University of Bahawalpur, Punjab, Pakistan
  • Muhammad Islam Crop Reporting Service, Agriculture Department, Punjab, Pakistan
  • Ashique Ali Chohan Department of Energy and Environment, Faculty of Agricultural Engineering, Sindh Agriculture University Tando Jam, Pakistan
  • Rashid Ahmed Department of Statistics, the Islamia University of Bahawalpur, Punjab, Pakistan
  • H. M. Muddasar Jamil Shera Crop Reporting Service, Agriculture Department, Punjab, Pakistan



Model Optimizations, Machine Learning Algorithms, Prediction Models, Performance Measurement


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.


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How to Cite

Malik Muhammad Hussain, Farrukh Shehzad, Muhammad Islam, Ashique Ali Chohan, Rashid Ahmed, & H. M. Muddasar Jamil Shera. (2023). Measuring the Performance of Supervised Machine Learning Algorithms for Optimizing Wheat Productivity Prediction Models: A Comparative Study. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 60(4), 35–44.



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