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Improving precision agriculture using integrated bio-inspired optimization models for crop recommendation in Rajasthan, India

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Abstract

This study introduces two novel hybrid nature-inspired optimization algorithms designed to enhance artificial neural network (ANN) performance in crop recommendation, leveraging remote sensing data from Landsat 8 and 9 platforms. The first hybrid approach combines the Gravitational Search Algorithm (GSA) with the Hunger Games Search (HGS) algorithm, promoting an improved balance between exploration and exploitation through gravitational dynamics and competitive resource-seeking strategies. The second hybrid integrates Electric Eel Foraging Optimization (EEFO) with Crested Porcupine Optimization (CPO), leveraging the foraging adaptability of electric eels and the defensive spatial strategies of crested porcupines to refine search efficiency and convergence stability. These hybrid algorithms were applied to classify crops across Kharif and Rabi seasons in Rajasthan, India. Experimental results reveal that the GSA-HGS hybrid achieves classification accuracies of 95.32% for Kharif and 94.99% for Rabi seasons, while the EEFO-CPO hybrid attains 94.09% and 94.94%, respectively. These findings demonstrate the potential of bio-inspired optimization strategies to support intelligent crop recommendation systems and advance precision agriculture practices in data-scarce agricultural regions.

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Data availability

Data used in this research is developed by the first author and it can be made available on reasonable request by sending email to her.

References

  1. Goel, L. An extensive review of computational intelligence-based optimization algorithms: trends and applications. Soft. Comput. 24(21), 16519–16549 (2020).

    Google Scholar 

  2. Goel, L., Jindal, A., & Mathur, S. Design and implementation of a crop recommendation system using nature-inspired intelligence for rajasthan, india, in: Deep Learning for Sustainable Agriculture, 109–128 (Elsevier, 2022).

  3. Sathya, S., & SU, R. et al., Season and production based crop recommendations system for agricultural data using machine learning technique, Mohammad Shaheer and S, Dharaneesh and SU, RamPrakash, Season and Production Based Crop Recommendations System for Agricultural Data Using Machine Learning Technique (2023).

  4. Bandara, P. et al. Crop recommendation system. International Journal of Computer Applications 975, 8887 (2020).

    Google Scholar 

  5. Wang, X., Bighorn sheep optimization algorithm: a novel and efficient approach for wireless sensor network coverage optimization, Physica Scripta (2025).

  6. Ghasemi, M. et al. Birds of prey-based optimization (bpbo): A metaheuristic algorithm for optimization. Evol. Intel. 18(4), 88 (2025).

    Google Scholar 

  7. Mohammed, B. O., Aghdasi, H. S. & Salehpour, P. Dhole optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Clust. Comput. 28(7), 430 (2025).

    Google Scholar 

  8. Wang, X., & Yao, L. Cape lynx optimizer: A novel metaheuristic algorithm for enhancing wireless sensor network coverage, Measurement 118361 (2025) .

  9. Jácome Galarza, L., Realpe, M., Viñán-Ludeña, M. S., Calderón, M. F. & Jaramillo, S. Agritransformer: A transformer-based model with attention mechanisms for enhanced multimodal crop yield prediction. Electronics 14(12), 2466 (2025).

    Google Scholar 

  10. Song, C. et al. Wheat yield prediction based on parallel cnn-lstm-attention with transfer learning model. Agriculture 15(23), 2519 (2025).

    Google Scholar 

  11. Afzal, H. et al. Incorporating soil information with machine learning for crop recommendation to improve agricultural output. Sci. Rep. 15(1), 8560 (2025).

    Google Scholar 

  12. Senapaty, M. K., Ray, A. & Padhy, N. A decision support system for crop recommendation using machine learning classification algorithms. Agriculture 14(8), 1256 (2024).

    Google Scholar 

  13. D. Yadav, An effective approach for crop recommendation with using features of specific locations and seasons and maximize crop yield production by using machine learning (2024). http://dx.doi.org/10.13140/RG.2.2.14590.40003 .

  14. Buttar, P. K. et al. Satellite imagery analysis for crop type segmentation using u-net architecture. Procedia Computer Science 235, 3418–3427 (2024).

    Google Scholar 

  15. Buttar, P. K. & Sachan, M. K. Semantic segmentation of satellite images for crop type identification in smallholder farms. J. Supercomput. 80(2), 1367–1395 (2024).

    Google Scholar 

  16. Jia, Z. et al. An improved crop yield prediction using cnn-bilstm model with attention mechanism. Journal of the ASABE 67(6), 1459–1467 (2024).

    Google Scholar 

  17. Kiruthika, S. & Karthika, D. Iot-based professional crop recommendation system using a weight-based long-term memory approach. Measurement: Sensors 27, 100722 (2023).

    Google Scholar 

  18. Mala, D. J., Basak, S., & Reynold, A. P. Intellifarmassist–a novel machine learning integrated genetic algorithm based optimal crop recommendation system, in: International Conference on Intelligent Systems Design and Applications, 42–54 (Springer, 2023).

  19. Gopi, P., & Karthikeyan, M. Intelligent crop recommendation with yield prediction using dragonfly algorithm based deep learning model, in: 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), 880–885 (IEEE, 2023).

  20. B. Jayanthi, et al., Coimbatore meteorlogical data based phenology stage prediction with ndvi with respect vegetables of horticulture crops., Journal of Namibian Studies 33 (2023).

  21. Ezhilarasi, T. P. & Rekha, K. S. Improved fuzzy ant colony optimization to recommend cultivation in tamil nadu, india. Acta Geophys. 70(6), 2873–2887 (2022).

    Google Scholar 

  22. Zamani, H. Evolutionary salp swarm algorithm with multi-search strategies and advanced memory mechanism for solving global optimization and complex engineering problems. Sci. Rep. 15(1), 1–41 (2025).

    Google Scholar 

  23. Zamani, H., Nadimi-Shahraki, M. H. & Gandomi, A. H. Qana: Quantum-based avian navigation optimizer algorithm. Eng. Appl. Artif. Intell. 104, 104314 (2021).

    Google Scholar 

  24. Sharma, S. K., Goel, L., & Mittal, N., Hybridizing earthquake dynamics-based optimization with multiple adaptative differential evolution: Towards a faster convergence metaheuristic, in: International Conference on Data Mining and Big Data, 250–263 (Springer, 2024).

  25. Goel, L., Gupta, D. & Panchal, V. Hybrid bio-inspired techniques for land cover feature extraction: A remote sensing perspective. Appl. Soft Comput. 12(2), 832–849 (2012).

    Google Scholar 

  26. Goel, L., Swamy, M., & Mantri, R. Swarm and artificial immune system-based intelligence techniques for geo-spatial feature extraction, in: Proceedings of International Conference on Computational Intelligence and Data Engineering: ICCIDE 2017, 65–84 (Springer, 2017).

  27. Rashedi, E., Nezamabadi-Pour, H. & Saryazdi, S. Gsa: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009).

    Google Scholar 

  28. Goel, L. Path extraction and planning for intelligent battlefield preparation using particle swarm optimization, gravitational search algorithm, and genetic algorithm, in: Proceedings of International Conference on Intelligent Cyber-Physical Systems: ICPS 2021, 77–89 (Springer, 2022).

  29. Yang, Y., Chen, H., Heidari, A. A. & Gandomi, A. H. Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst. Appl. 177, 114864 (2021).

    Google Scholar 

  30. Nguyen, H. & Bui, X.-N. A novel hunger games search optimization-based artificial neural network for predicting ground vibration intensity induced by mine blasting. Nat. Resour. Res. 30(5), 3865–3880 (2021).

    Google Scholar 

  31. Zhao, W. et al. Electric eel foraging optimization: A new bio-inspired optimizer for engineering applications. Expert Syst. Appl. 238, 122200 (2024).

    Google Scholar 

  32. Abdel-Basset, M., Mohamed, R. & Abouhawwash, M. Crested porcupine optimizer: A new nature-inspired metaheuristic. Knowl.-Based Syst. 284, 111257 (2024).

    Google Scholar 

  33. Goel, L., & Mishra, A. A survey of recent deep learning algorithms used in smart farming, in,. IEEE region 10 symposium (TENSYMP). IEEE 2022, 1–6 (2022).

  34. Mishra, A., & Goel, L. Optimizing artificial neural network for demography based crop recommendation: An ocean water current inspired approach in precision agriculture, International Journal of Information Technology, 1–18 (2024) .

  35. Hashjin, N. M., Amiri, M. H., Beheshti, A., & Najafabadi, M. K., Q2ho-mftv: A binary hippopotamus optimization algorithm for feature selection with a brief review of binary optimization, Knowledge-Based Systems, 114119 (2025) .

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Funding

The authors like to express their sincere gratitude to the Department of Science and Technology, Anusandhan National Research Foundation (ANRF/SERB) under the Core Research Grant Scheme (File no. DST/CRG/2022/000472 and sanction order ANRF/F/8568/2024-2025) for giving financial support at the Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, JLN Marg, Jaipur, Rajasthan, 302017, India

    Lavika Goel, Nawdeep Kumar, Snehal Jain & Yuganshi Agrawal

Authors

  1. Lavika Goel
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  2. Nawdeep Kumar
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  3. Snehal Jain
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  4. Yuganshi Agrawal
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Contributions

Lavika Goel: Writing original draft, implementation, validation, data collection, supervision. Nawdeep Kumar: Writing original draft, methodology, implementation, validation. Snehal Jain: Writing, validation. Yuganshi Aggarwal: Writing, validation.

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Correspondence to
Nawdeep Kumar.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Goel, L., Kumar, N., Jain, S. et al. Improving precision agriculture using integrated bio-inspired optimization models for crop recommendation in Rajasthan, India.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-37863-7

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  • DOI: https://doi.org/10.1038/s41598-026-37863-7

Keywords

  • Precision agriculture
  • Crop recommendation
  • Nature-inspired optimization algorithms
  • Hybrid algorithms
  • Artificial neural networks
  • Landsat 8/9

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