Predicting Crop Yield Productivity Using Machine Learning Algorithms: A Comparison of Linear and Non-linear Approaches
DOI:
https://doi.org/10.59994/ajbtme.2024.1.1Abstract
Predicting crop yield productivity is crucial for farmers and the agricultural sector to gain insights into crop productivity and returns. With advancements in technology and artificial intelligence, predicting crop yield using machine learning algorithms has become an important innovation. This study aimed to predict crop yield productivity using various machine learning algorithms and techniques. The dataset was sourced from Kaggle, preprocessed, and analyzed using linear algorithms such as Linear Regression, LASSO, and Ridge, as well as non-linear algorithms including SVR, KNN Regressor, and Polynomial Regression. Mean Squared Error (MSE) was computed to evaluate algorithms performance. Comparing the efficacy of linear versus non-linear algorithms on the dataset revealed that non-linear algorithms outperformed linear ones, indicating that the dataset's non-linear nature. Therefore, non-linear machine learning algorithms like SVR, KNN Regressor, and Polynomial Regression were recommended for better accuracy. Among these, KNN Regressor performed the best with an MSE of 0.00025, followed by SVR and Polynomial Regression with MSE values of 0.00142 and 0.0024, respectively.
Keywords:
Crop Yield Productivity, Machine Learning, Regression, SVR, Regression KNN, LASSO, Ridge, Polynomial RegressionReferences
Abbas, F., Afzaal, H., Farooque, A. A., & Tang, S. (2020). Crop yield prediction through proximal sensing and machine learning algorithms. Agronomy, 10(7), 1046. DOI: https://doi.org/10.3390/agronomy10071046
Bathla, G. (2020, November). Stock Price prediction using LSTM and SVR. In 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 211-214). IEEE. DOI: https://doi.org/10.1109/PDGC50313.2020.9315800
Elavarasan, D., & Vincent, P. D. (2020). Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE access, 8, 86886-86901. DOI: https://doi.org/10.1109/ACCESS.2020.2992480
Dash, C. S. K., Behera, A. K., Dehuri, S., & Ghosh, A. (2023). An outliers detection and elimination framework in classification task of data mining. Decision Analytics Journal, 6, 100164. DOI: https://doi.org/10.1016/j.dajour.2023.100164
Hu, T., Zhang, X., Bohrer, G., Liu, Y., Zhou, Y., Martin, J., ... & Zhao, K. (2023). Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield. Agricultural and Forest Meteorology, 336, 109458. DOI: https://doi.org/10.1016/j.agrformet.2023.109458
Iniyan, S., Varma, V. A., & Naidu, C. T. (2023). Crop yield prediction using machine learning techniques. Advances in Engineering Software, 175, 103326. DOI: https://doi.org/10.1016/j.advengsoft.2022.103326
Jhajharia, K., Mathur, P., Jain, S., & Nijhawan, S. (2023). Crop yield prediction using machine learning and deep learning techniques. Procedia Computer Science, 218, 406-417. DOI: https://doi.org/10.1016/j.procs.2023.01.023
Joseph, V. R., & Vakayil, A. (2022). SPlit: An optimal method for data splitting. Technometrics, 64(2), 166-176. DOI: https://doi.org/10.1080/00401706.2021.1921037
Kheir, A., Nangia, V., Elnashar, A., Devakota, M., Omar, M., Feike, T., & Govind, A. (2024). Developing automated machine learning approach for fast and robust crop yield prediction using a fusion of remote sensing, soil, and weather dataset. Environmental Research Communications. DOI: https://doi.org/10.1088/2515-7620/ad2d02
Kumar, Y. J. N., Spandana, V., Vaishnavi, V. S., Neha, K., & Devi, V. G. R. R. (2020, June). Supervised machine learning approach for crop yield prediction in agriculture sector. In 2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 736-741). IEEE. DOI: https://doi.org/10.1109/ICCES48766.2020.9137868
Manjunath, M. C., & Palayyan, B. P. (2023). An Efficient Crop Yield Prediction Framework Using Hybrid Machine Learning Model. Revue d'Intelligence Artificielle, 37(4). DOI: https://doi.org/10.18280/ria.370428
Maulud, D., & Abdulazeez, A. M. (2020). A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends, 1(2), 140-147. DOI: https://doi.org/10.38094/jastt1457
Morales, A., & Villalobos, F. J. (2023). Using machine learning for crop yield prediction in the past or the future. Frontiers in Plant Science, 14, 1128388. DOI: https://doi.org/10.3389/fpls.2023.1128388
Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists. " O'Reilly Media, Inc.".
Öngelen, G., & İnkaya, T. (2023). LOF weighted KNN regression ensemble and its application to a die manufacturing company. Sādhanā, 48(4), 246. DOI: https://doi.org/10.1007/s12046-023-02283-0
Paudel, D., Boogaard, H., de Wit, A., Janssen, S., Osinga, S., Pylianidis, C., & Athanasiadis, I. N. (2021). Machine learning for large-scale crop yield forecasting. Agricultural Systems, 187, 103016. DOI: https://doi.org/10.1016/j.agsy.2020.103016
Rashid, M., Bari, B. S., Yusup, Y., Kamaruddin, M. A., & Khan, N. (2021). A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction. IEEE access, 9, 63406-63439. DOI: https://doi.org/10.1109/ACCESS.2021.3075159
Shafi, U., Mumtaz, R., Anwar, Z., Ajmal, M. M., Khan, M. A., Mahmood, Z., ... & Jhanzab, H. M. (2023). Tackling food insecurity using remote sensing and machine learning based crop yield prediction. IEEE Access. DOI: https://doi.org/10.1109/ACCESS.2023.3321020
Shahhosseini, M., Hu, G., Huber, I., & Archontoulis, S. V. (2021). Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt. Scientific reports, 11(1), 1606. DOI: https://doi.org/10.1038/s41598-020-80820-1
Siddiqi, S., Qureshi, F., Lindstaedt, S., & Kern, R. (2023). Detecting Outliers in Non-IID Data: A Systematic Literature Review. IEEE Access. 70333-70352. DOI: https://doi.org/10.1109/ACCESS.2023.3294096
Van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, 105709. DOI: https://doi.org/10.1016/j.compag.2020.105709