Hybrid Machine Learning Models for Optimized Potato Price Prediction

Liping  Liu

doi:10.6180/jase.202605_29(5).0010

Hybrid Machine Learning Models for Optimized Potato Price Prediction

Research Categories

Liping LiuThis email address is being protected from spambots. You need JavaScript enabled to view it.

School of Management, Wuhan College, Wuhan 430212, Hubei, China

Received: September 29, 2024
Accepted: April 4, 2025
Publication Date: September 6, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202605_29(5).0010

Accurate prediction of agricultural commodity prices, such as potatoes, is crucial for enhancing market efficiency, supporting supply chain decisions, and ensuring economic stability in the agricultural sector. This study proposes an enhanced machine learning framework for potato price prediction using Light Gradient Boosting Regression (LGBR), optimized through two metaheuristic algorithms: the Stochastic Paint Optimizer (SPO) and the Population-based Vortex Search Algorithm (PVSA). The hybrid models LGSP (LGBR+SPO) and LGPB (LGBR+PVSA) were developed to reduce prediction error and improve generalization. Experimental results demonstrate that the optimized models outperform the baseline LGBR model. Specifically, LGPB achieved the lowest training mean squared error (MSE) of 3.33E+03, though it increased to 6.30E+03 in validation, indicating a potential overfitting issue. LGSP achieved moderate performance with a training MSE of 5.35E+03 and validation MSE of 7.77E+03. In contrast, the baseline LGBR model had the highest MSE values in both training (1.13E+04) and validation (1.34E+04), reflecting weaker predictive accuracy. Uncertainty measures (U95) followed a similar trend. The findings confirm that metaheuristic optimization can significantly improve regression performance in price forecasting tasks. However, challenges in model generalization highlight the need for further tuning and diverse datasets.

Keywords: Potato Prices, Decision-Making Process, Machine Learning, Light Gradient Boosting Regression, Stochastic Paint Optimizer.

[1] J. E. da Silva Ribeiro, A. G. C. da Silva, J. V. L. Lima, P. H. de Almeida Oliveira, E. dos Santos Coêlho, L. M. da Silveira, and A. P. B. Júnior, (2024) “Leaf area prediction of sweet potato cultivars: An approach to a non-destructive and accurate method" South African Journal of Botany 172: 42–51. DOI: https://doi.org/ 10.1016/j.sajb.2024.07.006.
[2] X. Lei, X. Xu, and S. Zhou, (2025) “Potato Yield Prediction Research Based on Improved Artificial Neural Net works Using Whale Optimization Algorithm" Potato Research 68: 1717–1726. DOI: https: //doi.org/10.1007/s11540-024-09819-9.
[3] M. Hassan, K. Khosravi, A. A. Farooque, T. J. Esau, A. Boluwade, and R. Sadiq, (2024) “Prediction of car bon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms–Nexus of field data and modelling" Smart Agri cultural Technology 9: 100559. DOI: https: //doi.org/10.1016/j.atech.2024.100559.
[4] P. Chaukhande, S. K. Luthra, R. N. Patel, S. R. Padhi, P. Mankar, M. Mangal, J. K. Ranjan, A. U. Solanke, G. P. Mishra, and D. C. Mishra, (2024) “Development and validation of near-infrared reflectance spectroscopy prediction modeling for the rapid estimation of biochemical traits in potato" Foods 13: 1655. DOI: https: //doi.org/10.3390/foods13111655.
[5] Y. Wang, Y. Xu, X. Wang, H. Wang, S. Liu, S. Chen, and M.Li, (2024) “Optimizing the effects of potato size and shape on near-infrared prediction models of potato quality using a linear-nonlinear algorithm" Journal of Food Composition and Analysis 135: 106679. DOI: https: //doi.org/10.1016/j.jfca.2024.106679.
[6] P. Jha, D. Dembla, and W. Dubey, (2024) “Deep learning models for enhancing potato leaf disease prediction: Implementation of transfer learning based stacking ensemble model" Multimedia Tools and Applications 83: 37839–37858. DOI: https: //doi.org/10.1007/s11042-023-16993-4.
[7] E.-S. M. El-Kenawy, A. A. Alhussan, N. Khodadadi, S. Mirjalili, and M. M. Eid, (2025) “Predicting potato crop yield with machine learning and deep learning for sustainable agriculture" Potato Research 68: 759–792. DOI: https: //doi.org/10.1007/s11540-024-09753-w.
[8] S. A. Alzakari, A. A. Alhussan, A.-S. T. Qenawy, A. M. Elshewey, and M.Eed, (2025) “An enhanced long short term memory recurrent neural network deep learning model for potato price prediction" Potato Research 68: 621–639. DOI: https: //doi.org/10.1007/s11540-024-09744-x.
[9] A.Gupta,A.Chug,andA.P.Singh,(2024)“Potatodis ease prediction using machine learning, image processing and IoT–a systematic literature survey" Journal of Crop Improvement 38: 95–137. DOI: https: //doi.org/10.1080/15427528.2023.2285827.
[10] A. A. Abdelhamid, A. A. Alhussan, A.-S. T. Qenawy, A. M. Osman, A. M. Elshewey, and M. Eed, (2024) “Potato harvesting prediction using an Improved ResNet 59 model" Potato Research: 1–20. DOI: https: //doi.org/10.1007/s11540-024-09773-6.
[11] M. Piekutowska and G. Niedbała, (2025) “Review of methods and models for potato yield prediction" Agri culture 15: 367. DOI: https: //doi.org/10.3390/agriculture15040367.
[12] A. Mukiibi, A. T. B. Machakaire, A. C. Franke, and J. M. Steyn, (2025) “A systematic review of vegetation in dices for potato growth monitoring and tuber yield predic tion from remote sensing" Potato research 68: 409–448. DOI: https: //doi.org/10.1007/s11540-024-09748-7.
[13] S. K. Seelan, S. Laguette, G. M. Casady, and G. A. Seielstad, (2003) “Remote sensing applications for precision agriculture: A learning community approach" Re motesensingofenvironment88:157–169. DOI: https: //doi.org/10.1016/j.rse.2003.04.007.
[14] H. Lotze-Campen, C. Müller, A. Bondeau, S. Rost, A. Popp, and W. Lucht, (2008) “Global food demand, productivity growth, and the scarcity of land and water resources: a spatially explicit mathematical programming approach" Agricultural Economics 39: 325–338. DOI: https: //doi.org/10.1111/j.1574-0862.2008.00336.x.
[15] R. FR. “Balancing water uses: water for food and water for nature. Thematic background paper”. In: International Conference on Freshwater. December 2001, Bonn, Germany. 2001. \
[16] G. C. Nelson, H. Valin, R. D. Sands, P. Havlík, H. Ahammad, D. Deryng, J. Elliott, S. Fujimori, T. Hasegawa, and E. Heyhoe, (2014) “Climate change effects on agriculture: Economic responses to biophysical shocks" Proceedings of the National Academy of sci ences 111: 3274–3279. DOI: https: //doi.org/10.1073/pnas.1222465110.
[17] P. Loudjani, (2014) “Precision Agriculture: An Opportunity for EU-Farmers–Potential Support with the CAP 2014-2020": 1–50.
[18] R. Gebbers and V. I. Adamchuk, (2010) “Precision agriculture and food security" Science 327: 828–831. DOI: https: //doi.org/10.1126/science.1183899.
[19] R. Raymundo, S. Asseng, R. Robertson, A. Petsakos, G. Hoogenboom, R. Quiroz, G. Hareau, and J. Wolf, (2018) “Climate change impact on global potato production" European Journal of Agronomy 100: 87–98. DOI: https: //doi.org/10.1016/j.eja.2017.11.008.
[20] A.Devaux,P.Kromann,andO.Ortiz,(2014)“Potatoes for sustainable global food security" Potato research 57: 185–199. DOI: https: //doi.org/10.1007/s11540-014-9265-1.
[21] L. Sharma, S. K. Bali, and J. D. Dwyer, (2017) “Study of Improving Yield Prediction and Sulfur Deficiency Detection Using Optical Sensors" ASA, CSSA and SSSA International Annual (2017): DOI: http: //dx.doi.org/10.3390/s17051095.
[22] S. Wolfert, L. Ge, C. Verdouw, and M.-J. Bogaardt, (2017) “Big data in smart farming–a review" Agricultural systems 153: 69–80. DOI: https: //doi.org/10.1016/j.agsy.2017.01.023.
[23] D.ZhangandJ.J. P. Tsai. Advances in machine learning applications in software engineering. IgiGlobal, 2006. DOI: http: //dx.doi.org/10.4018/978-1-59140-941-1.