Unsupervised Learning for Crop Suitability Clustering Based on Soil Nutrients
DOI:
https://doi.org/10.11113/mjfas.v21n6.4715Keywords:
Clustering Analysis, Data mining, K-Means clustering, Crops segmentation, Precision agriculture, Soil crop compatibilityAbstract
Soil-based crop recommendation plays a critical role in precision agriculture, especially under increasing climate uncertainty and resource limitations. This study proposes a clustering-based framework that leverages unsupervised machine learning to group crops according to soil parameters. A labelled dataset of 2,201 soil samples covering 22 crop types was analysed to uncover patterns linking soil profiles with crop suitability. The results reveal clear distinctions among crop groups, with generalist crops like rice and maize appearing across multiple clusters, while crops such as apple and grape form tighter, more specific groupings. These insights highlight natural affinities between soil chemistry and crop behaviour, offering a practical, data-driven basis for region-specific crop planning and soil resource optimization. The study contributes toward scalable, interpretable decision tools for sustainable agriculture, particularly in environments where efficient land and input management are critical. Unlike prior studies that employ clustering generically, this work comparatively evaluates multiple unsupervised algorithms under agricultural conditions, integrating soil nutrient dynamics into interpretable cluster formation.
References
Singh, K., Yadav, M., Barak, D., Bansal, S., & Moreira, F. (2025). Machine-learning-based frameworks for reliable and sustainable crop forecasting. Sustainability, 17(10), 4711.
Senoo, E. E. K., Anggraini, L., Kumi, J. A., Karolina, L. B., Akansah, E., Sulyman, H. A., Mendonça, I., & Aritsugi, M. (2024). IoT solutions with artificial intelligence technologies for precision agriculture: Definitions, applications, challenges, and opportunities. Electronics, 13(10), 1894.
Afzal, H., Amjad, M., Raza, A., Munir, K., Villar, S. G., Lopez, L. A. D., & Ashraf, I. (2025). Incorporating soil information with machine learning for crop recommendation to improve agricultural output. Scientific Reports, 15(1), 8560.
Eze, V. H. U., Eze, E. C., Alaneme, G. U., Bubu, P. E., Nnadi, E. O. E., & Okon, M. B. (2025). Integrating IoT sensors and machine learning for sustainable precision agroecology: Enhancing crop resilience and resource efficiency through data-driven strategies, challenges, and future prospects. Discover Agriculture, 3(1), 1–34.
Yang, H., Lim, H., Moon, H., Li, Q., Nam, S., Kim, J., & Choi, H. T. (2022). Simple optimal sampling algorithm to strengthen digital soil mapping using the spatial distribution of machine learning predictive uncertainty: A case study for field capacity prediction. Land, 11(11), 2098.
Folorunso, O., Ojo, O., Busari, M., Adebayo, M., Adejumobi, J., Folorunso, D., Ayo, F., Alabi, O., & Olabanjo, O. (2025). GeaGrow: A mobile tool for soil nutrient prediction and fertilizer optimization using artificial neural networks. Frontiers in Sustainable Food Systems, 9, 1533423.
Ennaji, O., Vergütz, L., & El Allali, A. (2023). Machine learning in nutrient management: A review. Artificial Intelligence in Agriculture, 9, 1–11.
Khan, M. H. (2022). Modelling the long-term impact of modernized irrigation systems on soil water and salt balances, and crop water productivity. Doctoral dissertation, Massey University.
Pham, T.-H., Acharya, P., Bachina, S., Osterloh, K., & Nguyen, K.-D. (2024). Deep-learning framework for optimal selection of soil sampling sites. Computers and Electronics in Agriculture, 217, 108650.
Salloum, S. A., Masa’deh, R., Al-Zoghb, A. M., & Shaalan, K. (2025). Optimizing crop recommendation using machine learning: An analytical study based on smart agricultural data. In Generative AI in Creative Industries (pp. 637–650). Springer Nature Switzerland.
Islam, M. R., Oliullah, K., Kabir, M. M., Alom, M., & Mridha, M. F. (2023). Machine learning enabled IoT system for soil nutrients monitoring and crop recommendation. Journal of Agriculture and Food Research, 14, 100880.
Saki, M., Keshavarz, R., Franklin, D., Abolhasan, M., Lipman, J., & Shariati, N. (2024). Precision soil quality analysis using transformer-based data fusion strategies: A systematic review. arXiv, arXiv:2410.18353.
Sethi, S., & Lakhina, U. (2024). Intelligent crop selection and soil nutrient management using machine learning. In 2024 International Conference on Computational Intelligence and Computing Applications (ICCICA) (Vol. 1, pp. 459–464). IEEE.
Zhu, F., Zhu, C., Fang, Z., Lu, W., & Pan, J. (2025). Using constrained K-means clustering for soil texture mapping with limited soil samples. Agronomy, 15(5), 1220.
Bougiouklis, J.-N., Barouchas, P. E., Petropoulos, P., Tsesmelis, D. E., & Moustakas, N. (2025). Precision soil sampling strategy for the delineation of management zones in olive cultivation using unsupervised machine learning methods. Scientific Reports, 15(1), 8253.
Gulhane, V. A., Rode, S. V., & Pande, C. B. (2023). Correlation analysis of soil nutrients and prediction model through ISO cluster unsupervised classification with multispectral data. Multimedia Tools and Applications, 82(2), 2165–2184.
Zhao, W., Unagaev, A., & Efremova, N. (2025). Data-driven soil organic carbon sampling: Integrating spectral clustering with conditioned Latin hypercube optimization. arXiv, arXiv:2506.10419.
Novielli, P., Magarelli, M., Romano, D., de Trizio, L., Di Bitonto, P., Monaco, A., Amoroso, N., et al. (2024). Climate change and soil health: Explainable artificial intelligence reveals microbiome response to warming. Machine Learning and Knowledge Extraction, 6(3), 1564–1578.
Swain, K. P., Nayak, S. R., Ravi, V., Mishra, S., Alahmadi, T. J., Singh, P., & Diwakar, M. (2024). Empowering crop selection with ensemble learning and K-means clustering: A modern agricultural perspective. The Open Agriculture Journal, 18(1).
Muhammed, D., Ahvar, E., Ahvar, S., & Trocan, M. (2023). Performance evaluation of machine learning algorithms for a cluster-based crop recommendation system. In 2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (pp. 441–445). IEEE.
Wang, H., Zhao, Y., Li, S., Liu, Z., & Zhang, X. (2025). DeepCropClustering: A deep unsupervised clustering approach by adopting nearest and farthest neighbors for crop mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 224, 187–201.
Kulatunga, C., Dhelim, S., & Kechadi, T. (2024). Machine learning for dynamic management zone in smart farming. arXiv, arXiv:2408.00789.
Chaali, N., Ramírez-Gómez, C. M., Jaramillo-Barrios, C. I., Garré, S., Barrero, O., Ouazaa, S., & Calderon Carvajal, J. E. (2024). Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production. Smart Agricultural Technology, 9, 100635.
Prity, F. S., Hasan, M. M., Saif, S. H., Hossain, M. M., Bhuiyan, S. H., Islam, M. A., & Lavlu, M. T. H. (2024). Enhancing agricultural productivity: A machine learning approach to crop recommendations. Human-Centric Intelligent Systems, 1–14.
Zhao, K., Wu, S., Liu, C., Wu, Y., & Efremova, N. (2023). Precision agriculture: Crop mapping using machine learning and Sentinel-2 satellite imagery. arXiv, arXiv:2403.09651.
Nagy, J., Zalai, M., Illés, Á., & Monoki, S. (2024). The impact of crop year and crop density on the production of sunflower in site-specific precision farming in Hungary. Agriculture, 14(9), 1515.
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Copyright (c) 2025 Huma Jamshed, Urooj Waheed, Yusra Mansoor, Ahmad Hussain
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