A Comparative Review of Boosting Algorithms for Agricultural Applications: Performance Analysis, Algorithm Selection Framework, and Future Directions

Assadullah Soomro; Mushtaque Ahmed Rahu; Sayed Mazhar Ali; Sarang Karim

A Comparative Review of Boosting Algorithms for Agricultural Applications

Performance Analysis, Algorithm Selection Framework, and Future Directions

Authors

Assadullah Soomro Department of Electrical Engineering, Sukkur Institute of Business Administration University, Sukkur, Pakistan
Mushtaque Ahmed Rahu PhD in Electronic Engineering, Department of Electronic Engineering, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan
Sayed Mazhar Ali Lecturer, Department of Mechatronics Engineering, Air University Karachi Campus, Karachi, Pakistan
Sarang Karim Assistant Professor, Department of Telecommunication Engineering, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan

Keywords:

Agriculture, Boosting, Ensemble Learning, AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost

Abstract

The flexibility of Machine Learning algorithms, their automation, and the capability to address big data have been heavily exploited in agricultural research. The most notable Machine Learning algorithms are the Boosting Algorithms, "Gathering wisdom in a group of Fools", thus turning weak learners into strong learners. Both high flexibility and interpretability are key features of Boosting algorithms. Through this work, we give insights into the characteristics of Boosting Algorithms to enable them to better exploit their strengths in agricultural research. This paper summarises recent developments in boosting algorithms, relevant applications in agriculture, and how the implementation of boosting algorithms and their use are related to their properties. This study demonstrates that great progress in the sphere of agriculture can be achieved in terms of explanation and interpretation, as well as in terms of predictive performance of the Boosting. This paper provides a detailed overview of the significant Boosting algorithms used in agriculture, like AdaBoost, Gradient Boosting Machines (GBM), XGBoost, LightGBM, CatBoost, and other successful variants. After analysing 45 peer-reviewed publications from 2015 to 2025, we compared the different algorithms in terms of their predictive accuracy, training speed, ability to deal with categorical data, overfitting control, and scalability and present a decision matrix for choosing the algorithms for specific agricultural applications, such as crop yield prediction, disease detection, and soil analysis. This study also gives a comparative summary to advise practitioners on the best algorithm to use in various applications, especially in agriculture. The paper has ended with unrestricted research direction and valuable suggestions to practitioners in the agricultural sector.

References

Sharma, A., Jain, A., Gupta, P., & Chowdary, V. (2021). Machine learning applications for precision agriculture: A comprehensive review. IEEE Access, 9, 48492-48528.

Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1249.

Molnar, C., Casalicchio, G., & Bischl, B. (2020).

Interpretable machine learning – A brief history, state-of-the-art and challenges. arXiv preprint, 2010.09337.

Abbas, F., Cai, Z., Shoaib, M., Iqbal, J., Ismail, M., Alrefaei, A. F., & Albeshr, M. F. (2024). Machine learning models for water quality prediction: a comprehensive analysis and uncertainty assessment in Mirpurkhas, Sindh, Pakistan. Water, 16(7), 941.

https://doi.org/10.3390/w16070941

Martinović, M., Dokic, K., & Pudić, D. (2025). Comparative analysis of machine learning models for predicting innovation outcomes: An applied AI approach. Applied Sciences, 15(7), Article 3636.

Malarvizhi, M. D., & Kiruthikas, M. (2025). Comparative study of boosting algorithms: Concepts, algorithms, applications and prospects. International Journal of Innovative Research in Technology, 11(3), 1816–1825.

Nguyen, N., & Ngo, D. (2025). Comparative analysis of boosting algorithms for predicting personal default. Cogent Economics & Finance, 13(1), 2465971.

Ganie, S. M., Pramanik, P. K. D., Malik, M. B., Nayyar, A., & Kwak, K. S. (2023). An improved ensemble learning approach for heart disease prediction using boosting algorithms. Computer Systems Science and Engineering, 46(3), 3993–4006.

Tyralis, H., & Papacharalampous, G. (2021). Boosting algorithms in energy research: a systematic review. Neural Computing and Applications, 33(21), 14101-14117.

Bijalwan, P., Gupta, A., Mendiratta, A., Johri, A., & Asif, M. (2024). Predicting the productivity of municipality workers: A comparison of six machine learning algorithms. Economies, 12(1), 16.

Liyungu, J., Yu, B., Walubita, L. F., Fisonga, M., Ling, M., Ninteretse, J. D. D., & Mwanaumo, E. M. (2026). Rubber particle size effect on rubberised concrete compressive strength: ensemble machine learning models validated by experiments. International Journal of Pavement Engineering, 27(1), 2662960.

Panthakkan, A., Gurjarand, A., Patel, J., & Patel, H. (2025). A Comparative Analysis of Ensemble Strategies. In Applications of Artificial Intelligence and Data Science: First Global Conference, AAIDS 2024, London, UK, April 3–5, 2024, Proceedings (p. 49). Springer Nature.

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146-3154.

Zhai, X., Liu, Y., Hong, Y., Yang, Y., Wang, P., Ye, Z., & Wang, Q. (2025). Improved digital mapping of soil texture using the kernel temperature–vegetation dryness index and adaptive boosting. Ecological Informatics, 87, 103083.

Rahu, M. A., Karim, S., Shams, R., Soomro, A. A., & Chandio, A. F. (2022). Wireless sensor networks-based smart agriculture: Sensing technologies, applications, and future directions. Sukkur IBA Journal of Emerging Technologies, 5(2), 18–32.

Rahu, M. A., Chandio, A. F., Aurangzeb, K., Karim, S., Alhussein, M., & Anwar, M. S. (2023). Toward the design of Internet of Things and machine learning-enabled frameworks for analysis and prediction of water quality. IEEE Access, 11, 101055–101086. https://doi.org/10.1109/ACCESS-2023.3315649.

Rahu, M. A., Shaikh, M. M., Karim, S., Chandio, A. F., Dahri, S. A., Soomro, S. A., & Ali, S. M. (2024). An IoT and machine learning solution for monitoring agricultural water quality: A robust framework. Mehran University Research Journal of Engineering and Technology, 43(1), 192-205.

Rahu, M. A., Shaikh, M. M., Karim, S., Chandio, A. F., Dahri, S. A., Soomro, S. A., & Ali, S. M. (2024). Water quality monitoring and assessment for efficient water resource management through the Internet of Things and machine learning approaches for agricultural irrigation. Water Resources Management, 38(10), 3845–3865. https://doi.org/10.1007/s11269-024-03899-5.

Rahu, Mushtaque Ahmed. "Transforming Farming with Technology: A Smart Novel Agriculture Framework and Infrastructure." Sukkur IBA Journal of Computing and Mathematical Sciences 8, no. 2 (2024): 53-69.

Karim, S., Hussain, K., Alvi, M. B., Rahu, M. A., Kaloi, M. A., & Haleem, H. (2025). Artificial Intelligence in Sustainable Smart Agriculture: Concepts, Applications, and Challenges. VAWKUM Transactions on Computer Sciences, 13(1), 307–342. https://doi.org/10.21015/vtcs.v13i1.2151

Hussain, M., Ali, S. M., Rahu, M. A., Tunio, N. A., & Chandio, A. F. (2025). IoT-Enabled Machine Learning Framework for Precision Agriculture: Achieving Near-Perfect Crop Yield Prediction in Pakistan’s Diverse Agro-Climatic Zones. VAWKUM Transactions on Computer Sciences, 13(2), 263–275. https://doi.org/10.21015/vtcs.v13i2.2310.

Rahu, M. A., Khilji, W. A., Ayaz, A., Memon, S. R., & Jatoi, I. K. (2026). Agriculture 6.0: Leveraging AI, IoT, machine learning, and blockchain for a sustainable future. International Journal of Agriculture Innovations and Cutting-Edge Research, 4(1), 50-64. https://jai.bwo-researches.com/index.php/jwr/article/view/203

Jatoi, I. K., Rahu, M. A., Memon, N., Aurangzaib, M., & Oad, U. (2026). Integrating new frontier digital twin technology in the smart agriculture revolution. International Journal of Agriculture Innovations and Cutting-Edge Research, 4(2), 1-13.

https://jai.bwo-researches.com/index.php/jwr/article/view/228.

Hussain, S., Ali, Z., & Ahmed, W. (2024). Comparative analysis of XGBoost and Random Forest for cotton yield prediction in semi-arid regions. International Journal of Agriculture Innovations and Cutting-Edge Research, 3(1), 22–38.

Schapire, R. E. (2013). Explaining AdaBoost. In B. Schölkopf, Z. Luo, & V. Vovk (Eds.), Empirical inference (pp. 37-52). Springer.

González-Sánchez, A., & Frausto-Solís, J. (2023). Hyperspectral image analysis for coffee pest detection using ensemble learning. Computers and Electronics in Agriculture, 214, 108321.

Oliveira, R. S., & Silva, L. C. (2023). Benchmarking boosting algorithms for digital soil organic carbon mapping at the regional scale. Geoderma, 430, 116334.

Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., & Nauss, T. (2019). Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software, 111, 1-13.

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2022). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 73, 311-345. (Revised edition)

Gupta, M., & Mani, S. (2022). Monotonic decision tree ensembles for risk-averse agricultural decisions. Artificial Intelligence in Agriculture, 6, 185-198.

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. (2) El-Rashidy, N., et al. (2022).

Bentéjac, C., Csörgő, A., & Martínez-Muñoz, G. (2021). A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3), 1937-1967. https://doi.org/10.1007/s10462-020-09896-5

A Comparative Review of Boosting Algorithms for Agricultural Applications