Volume 11 • Issue 2 • PP: 94–114 • 2026
Global Crop Yields Forecasting Using Informer Architecture Optimized with Football-Inspired Metaheuristic
Open Access & Copyright
© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
View PDF Abstract
Accurate forecasting of agricultural production is essential for managing food supply chains, guiding policy decisions, and building resilience against climate variability. However, modeling long-range, country-level crop production remains challenging due to temporal complexity, nonlinear dependencies, and the need for highly generalizable prediction systems. This study addresses these challenges by developing a hybrid forecasting framework that combines deep learning architectures with metaheuristic hyperparameter optimization. A global dataset spanning tomato and potato production from 1961 to 2021 was used to evaluate multiple forecasting models, including Informer, N-BEATS, LogTrans, N-HITS, EALSTM, TST, and LSTM. The Informer model achieved the best baseline performance (RMSE = 0.0799; NSE = 0.9070) and was selected for optimization. A comparative analysis was conducted using several metaheuristic algorithms, with particular focus on the Football Optimization Algorithm (FbOA), a novel strategy inspired by cooperative team dynamics. FbOA delivered the highest gains across all evaluated metrics, reducing RMSE to 0.00109, MSE to 1.19×10−6, and increasing NSE to 0.9260, R2 to 0.9600, and the correlation coefficient r to 0.9550. These results confirmed that metaheuristic tuning substantially enhances the forecasting capability of deep models, particularly when guided by domain-inspired search logic. The proposed framework demonstrates strong potential for integration into real-time, AI-driven agricultural decision support systems, offering scalable solutions for food security planning, climate-smart agriculture, and long-term sustainability forecasting.
Keywords
References
[1] M. Sangeetha, G. Thejaswini, A. Shoba, S. S. Gaikwad, R. T. Amretasre, and S. Nivedita, “Design and development of a crop quality monitoring and classification system using iot and blockchain,” Journal of Physics Conference Series, vol. 1964, pp. 62011–62011, 07 2021.
[2] L. Hou, Z. Liu, J. You, Y. Liu, J. Xiang, J. Zhou, and P. Yu, “Tomato sorting system based on machine vision,” Electronics, vol. 13, pp. 2114–2114, 05 2024.
[3] N. G. Abd El-Fattah, M. S. Abd El-baki, M. Maher, and S. Elsayed, “Integrating climate and plant variables with machine learning models to forecast tomato yield at different soil moisture levels,” Egyptian Journal of Soil Science, 2024.
[4] U. S, V. Muralibhaskaran, M. G, and V. G, “Forecasting of tomato prices and yield based on season and weather using lstm and arima algorithms,” in 2024 International Conference on Emerging Research in Computational Science (ICERCS), pp. 1–6, 2024.
[5] S. M. H. Khatibi and J. Ali, “Harnessing the power of machine learning for crop improvement and sustainable production,” 08 2024.
[6] X. Qin, B.Wu, H. Zeng, M. Zhang, and F. Tian, “Global gridded crop production dataset at 10 km resolution from 2010 to 2020,” Scientific Data, vol. 11, p. 1377, 2024.
[7] B. Bischl, M. Binder, M. Lang, T. Pielok, J. Richter, S. Coors, J. Thomas, T. Ullmann, M. Becker, A. Boulesteix, D. Deng, and M. Lindauer, “Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges,” Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, vol. 13, 01 2023.
[8] H. T. Nguyen, H.-Y. Jeong, K.-D. Kim, N. Xuan-Mung, N. Dao, H.-N. Tran, V.-K. Hoang, N. N. Anh, and M. T. Vu, “A new hyperparameter tuning framework for regression tasks in deep neural network: Combinedsampling algorithm to search the optimized hyperparameters,” Mathematics, vol. 12, pp. 3892–3892, 12 2024.
[9] C. Aviles Toledo, M. M. Crawford, and M. R. Tuinstra, “Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments,” Frontiers in Plant Science, vol. 15, p. 1408047, 2024.
[10] L. Franceschi, M. Donini, V. Perrone, A. Klein, C. Archambeau, M. Seeger, M. Pontil, and P. Frasconi, “Hyperparameter optimization in machine learning,” arXiv preprint arXiv:2410.22854, 2024.
[11] S. Barke, C. Poelitz, C. Negreanu, B. Zorn, J. Cambronero, A. Gordon, V. Le, E. Nouri, N. Polikarpova, A. Sarkar, B. Slininger, N. Toronto, and J. Williams, “Solving data-centric tasks using large language models,” in Findings of the Association for Computational Linguistics: NAACL 2024, pp. 626–638, Association for Computational Linguistics, 2024.
[12] H. Zhang, Y. Dong, C. Xiao, and M. Oyamada, “Jellyfish: Instruction-tuning local large language models for data preprocessing,” in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 8754–8782, Association for Computational Linguistics, 2024.
[13] L. R. Khot, S. Sankaran, A. H. Carter, D. A. Johnson, and T. F. Cummings, “Uas imaging-based decision tools for arid winter wheat and irrigated potato production management,” International Journal of Remote Sensing, vol. 37, no. 1, pp. 125–137, 2016.
[14] C. Singha and K. C. Swain, “Rice and potato yield prediction using artificial intelligence techniques,” in Internet of Things and Analytics for Agriculture, Volume 3 (P. K. Pattnaik, R. Kumar, and S. Pal, eds.), pp. 185– 199, Springer, 2022.
[15] K. A. Al-Gaadi, A. A. Hassaballa, E. Tola, A. G. Kayad, R. Madugundu, B. Alblewi, and F. Assiri, “Prediction of potato crop yield using precision agriculture techniques,” PLOS ONE, vol. 11, no. 9, p. e0162219, 2016.
[16] F. Ferracina, B. Krishnamoorthy, M. Halappanavar, S. Hu, and V. Sathuvalli, “Predictive analytics of varieties of potatoes,” arXiv preprint arXiv:2404.03701, 2024.
[17] D. Gómez, P. Salvador, J. Sanz, and J. L. Casanova, “Potato yield prediction using machine learning techniques and sentinel 2 data,” Remote Sensing, vol. 11, no. 15, p. 1745, 2019.
[18] D. Gómez, P. Salvador, J. Sanz, and J. L. Casanova, “New spectral indicator potato productivity index based on sentinel-2 data to improve potato yield prediction: a machine learning approach,” International Journal of Remote Sensing, vol. 42, no. 9, pp. 3426–3444, 2021.
[19] D. Li, Y. Miao, S. K. Gupta, C. J. Rosen, F. Yuan, C. Wang, L. Wang, and Y. Huang, “Improving potato yield prediction by combining cultivar information and uav remote sensing data using machine learning,” Remote Sensing, vol. 13, no. 16, p. 3322, 2021.
[20] S. K. Towfek and A. A. Alhussan, “Potato production forecasting based on balance dynamic biruni earth radius algorithm for long short-term memory models,” Potato Research, vol. 67, no. 4, pp. 1927–1963, 2024.
[21] A. A. Alhussan, D. S. Khafaga, M. Abotaleb, P. Mishra, and E.-S. M. El-Kenawy, “Global potato production forecasting based on time series analysis and advanced waterwheel plant optimization algorithm,” Potato Research, vol. 67, no. 4, pp. 1965–2000, 2024.
[22] P. Mishra, A. A. Alhussan, D. S. Khafaga, P. Lal, S. Ray, M. Abotaleb, K. Alakkari, M. M. Eid, and E.-S. M. Elkenawy, “Forecasting production of potato for a sustainable future: Global market analysis,” Potato Research, vol. 67, no. 4, pp. 1671–1690, 2024.
[23] K. Alibabaei, P. D. Gaspar, and T. M. Lima, “Crop yield estimation using deep learning based on climate big data and irrigation scheduling,” Energies, vol. 14, no. 11, p. Article 11, 2021.
[24] R. Raymundo, S. Asseng, R. Robertson, A. Petsakos, G. Hoogenboom, R. Quiroz, G. Hareau, and J. Wolf, “Climate change impact on global potato production,” European Journal of Agronomy, vol. 100, pp. 87–98, 2018.
[25] N. Mishra, S. Mishra, and H. K. Tripathy, “Rice yield estimation using deep learning,” in Innovations in Intelligent Computing and Communication (M. Panda, S. Dehuri, M. R. Patra, P. K. Behera, G. A. Tsihrintzis, S.-B. Cho, and C. A. C. Coello, eds.), pp. 379–388, Springer International Publishing, 2022.
[26] DeepSeek-AI, “Deepseek-v3 technical report,” arXiv preprint arXiv:2412.19437, 2024.
[27] H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang, “Informer: Beyond efficient transformer for long sequence time-series forecasting,” arXiv preprint arXiv:2012.07436, 2021.
[28] B. N. Oreshkin, D. Carpov, N. Chapados, and Y. Bengio, “N-beats: Neural basis expansion analysis for interpretable time series forecasting,” arXiv preprint arXiv:1905.10437, 2020.
[29] S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen, Y. Wang, and X. Yan, “Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting,” arXiv preprint arXiv:1907.00235, 2020.
[30] C. Challu, K. G. Olivares, B. N. Oreshkin, F. Garza, M. Mergenthaler-Canseco, and A. Dubrawski, “N-hits: Neural hierarchical interpolation for time series forecasting,” arXiv preprint arXiv:2201.12886, 2022.
[31] F. Kratzert, D. Klotz, G. Shalev, G. Klambauer, S. Hochreiter, and G. Nearing, “Towards learning universal, regional, and local hydrological behaviors via machine-learning applied to large-sample datasets,” arXiv preprint arXiv:1907.08456, 2019.
[32] G. Zerveas, S. Jayaraman, D. Patel, A. Bhamidipaty, and C. Eickhoff, “A transformer-based framework for multivariate time series representation learning,” in Proceedings of the ACM SIGKDD Conference on Knowledge Discovery & Data Mining, KDD ’21, (New York, NY, USA), p. 2114–2124, Association for Computing Machinery, 2021.
[33] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735– 1780, 1997.
[34] E.-S. M. El-Kenawy, F. H. Rizk, A. M. Zaki, M. E. Mohamed, A. Ibrahim, A. A. Abdelhamid, N. Khodadadi, E. M. Almetwally, and M. M. Eid, “Football optimization algorithm (fboa): A novel metaheuristic inspired by team strategy dynamics,” Journal of Artificial Intelligence and Metaheuristics, vol. 8, no. 1, pp. 21–38, 2024.
[35] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, pp. 1942–1948, 1995.
[36] S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, 2016.
[37] D. Simon, “Biogeography-based optimization,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 6, pp. 702–713, 2008.
[38] S. Mirjalili, S. Mirjalili, and A. Hatamlou, “Multi-verse optimizer: a nature-inspired algorithm for global optimization,” Neural Computing and Applications, vol. 27, 2015.
[39] X.-S. Yang, “A new metaheuristic bat-inspired algorithm,” in Studies in Computational Intelligence, vol. 284, 2010.
[40] H. Salimi, “Stochastic fractal search: A powerful metaheuristic algorithm,” Knowledge-Based Systems, vol. 75, 2014.
[41] R. Storn and K. Price, “Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, pp. 341–359, 1997.
[42] X. Wang, V. Snášel, S. Mirjalili, J.-S. Pan, L. Kong, and H. A. Shehadeh, “Artificial protozoa optimizer (apo): A novel bio-inspired metaheuristic algorithm for engineering optimization,” Knowledge-Based Systems, vol. 295, p. 111737, 2024.
Cite This Article
Choose your preferred format
Publisher's Note
The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.