IJSDIP | International Journal of System Design & Info Processing

Smart Farming Assistant: A Review of Fertilizer Recommendation Systems Based on Soil and Crop Data

Paper ID:

IJSDIP_2025_VOLUME 13_ISSUE 4_1-7

Author's Name:

Sanjog karan, Vinay jayswal, Aman maurya, Pratiksha Deshmukh

Volume:

Issue:

Issue 4

Year:

2025

Pages:

1-7

Abstract

Fertilizer recommendation is a critical component of precision agriculture, directly affecting crop productivity, soil health, and environmental sustainability. Traditional approaches such as manual soil testing and generalized guidelines often fail to address the variability of soil properties, climatic conditions, and crop-specific requirements. Recent advances in Machine Learning (ML), Artificial Intelligence (AI), and Internet of Things (IoT) have enabled the development of smart farming assistants that integrate soil nutrient data (NPK, pH, organic matter) with environmental parameters to provide site-specific fertilizer recommendations. This review presents a comprehensive analysis of fertilizer prediction models published between 2019 and 2025, highlighting supervised, unsupervised, semi-supervised, reinforcement, and hybrid ML approaches. Key algorithms such as Random Forest, Support Vector Machines, and XGBoost consistently demonstrate superior performance, while IoT-based frameworks enable real-time and automated recommendation pipelines. A comparative analysis of existing studies reveals challenges, including limited and region-specific datasets, high computational requirements, and barriers to farmer adoption. The paper also discusses publicly available datasets, identifies gaps in current research, and outlines future directions such as hybrid ensemble models, creation of benchmark soil–crop datasets, and development of affordable IoT solutions. The findings suggest that ML-driven fertilizer recommendation systems hold significant potential to enhance precision agriculture and support sustainable farming practices in India and beyond.

Keywords

machine learning, fertilizer recommendation, agriculture, soil analysis, crop prediction

References

[1] A. Deveshwar, “Overview of Agricultural Subsidies in India and Its Impact on Environment,” Current World Environment Journal, vol. 18, no. 3, pp. 776–786, Dec. 2023. [Online]. Available: https://www.cwejournal.org/article/poverview-of-agricultural-subsidies- in-india-and-its-impact-on-environmentp [2] “India’s Soil Health Crisis,” Drishti IAS, Feb. 23, 2025. Available: https://www.drishtiias.com/daily-updates/daily-news-editorials/india-s- soil-health-crisis [3] B. S. Das, "Soil health and its relationship with food security," Environmental Science and Policy, vol. 132, pp. 237-246, 2022. Available: https://www.sciencedirect.com/science/article/pii/S2667006222000375 [4] T. Muthukrishnan, V. Pandi, and K. Thiruvenkadam, “IoT-Enabled Smart Fertilizer Recommendation System Using Machine Learning,” International Journal of Creative Research Thoughts, vol. 13, no. 3, pp. 124–138, Mar. 2025. [Online]. Available: https://www.ijcrt.org/papers/IJCRTAY02019.pdf [5] Musanase, C., Vodacek, A., Hanyurwimfura, D., Uwitonze, A., & Kabandana, I. (2023). Data-driven analysis and machine learning-based crop and fertilizer recommendation system for revolutionizing farming practices. Agriculture, 13(11), 2141.https://doi.org/10.3390/agriculture13112141 [6] S. Singh, A. Sharma, and R. Kumar, "Generative AI based Fertilizer and Pesticide Recommendation System for Precision Agriculture," International Journal of Science and Research Archive, vol. 15, no. 2, pp. 1520-1530, 2025. [Online]. Available: https://journalijsra.com/sites/default/files/fulltext_pdf/IJSRA-2025- 1498.pdf [7] B. Dey, J. Ferdous, and R. Ahmed, “Machine learning-based recommendation of agricultural and horticultural crop farming in India under climate constraints,” Heliyon, vol. 10, no. 7, p. e2405844, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2405844024011435 [8] A. A. Khan, M. Faheem, R. N. Bashir, C. Wechtaisong and M. Z. Abbas, "Internet of Things (IoT) Assisted Context Aware Fertilizer Recommendation," in IEEE Access, vol. 10, pp. 129505-129519, 2022, doi: 10.1109/ACCESS.2022.3228160 [9] L. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, “Machine learning in agriculture: A review,” Sensors, vol. 18, no. 8, p. 2674, 2018. [Online]. Available: https://www.mdpi.com/1424- 8220/18/8/2674 [10] O. Ennaji, L. Vergütz, and A. El Allali, “Machine learning in nutrient management: A review,” Artificial Intelligence in Agriculture, vol. 9, pp. 1-11, Sep. 2023. https://doi.org/10.1016/j.aiia.2023.06.001 [11] Shastri, S., Kumar, S., Mansotra, V., & Salgotra, R. (2025). Advancing crop recommendation system with supervised machine learning and explainable artificial intelligence. Scientific Reports, 15(1), 25498. Available: https://doi.org/10.1038/s41598-025-07003-8 [12] Mohd Rahman, M. I., Mohd Razman, M. A., Abdul Majeed, A. P. P., Aiman Shapiee, M. N., Abdullah, M. A., & Musa, R. M. (2023). Unsupervised Fertigation and Machine Learning for Crop Vegetation Parameter Analysis. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 417-425. [13] P. Kumari, G. Y. Chandan, D. Agarwal et al., “Hybrid approaches in crop recommendation systems,” Agronomy, vol. 7, no. 11, pp. 30–40,2024.[Online].Available: https://www.agronomyjournals.com/article/view/1951/7-11-30 [14] Iniyan, S., Raja, M. S., Srinivasan, R., Santhanakrishnan, C., & Srinivasan, A. (2023, December). Crop and fertilizer recommendation system applying machine learning classifiers. In 2023 International Conference on Emerging Research in Computational Science (ICERCS) (pp. 1-6). IEEE.https://doi.org/10.1109/ICERCS57948.2023.10433972 [15] Ajagalla, M., Pandey, M., Choudhary, J. (2025). Optimizing Fertilizer Recommendations for Root Vegetable Crops Through Soil Feature Analysis and Environmental Parameter Integration Via Machine Learning Techniques. In: Nanda, S.J., Yadav, R.P., Gandomi, A.H., Saraswat, M. (eds) Data Science and Applications. ICDSA 2024. Lecture Notes in Networks and Systems, vol 1237. Springer, Singapore. https://doi.org/10.1007/978-981-96-1185-0_29 [16] Patel, K., & Patel, H. B. (2023). Multi-criteria Agriculture Recommendation System using Machine Learning for Crop and Fertilizer Prediction. Current Agriculture Research Journal. Available: http://www.agriculturejournal.org/volume11number1/multi-criteria- agriculture-recommendation-system-using-machine-learning-for-crop- and-fertilizers-prediction/ [17] Garg, D., & Alam, M. (2023). An effective crop recommendation method using machine learning techniques. International journal of advanced technology and engineering exploration, 10(102), 498 DOI:10.19101/IJATEE.2022.10100456 [18] Gosai, D., Raval, C., Nayak, R., Jayswal, H., & Patel, A. (2021). Crop recommendation system using machine learning. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(3), 558-569.DOI: https://doi.org/10.32628/CSEIT2173129 [19] Reddy, G. V., Reddy, M. V. K., Spandana, K., Subbarayudu, Y., Albawi, A., Chandrashekar, R., ... & Praveen, P. (2024). Precision farming practices with data-driven analysis and machine learning-based crop and fertiliser recommendation system. In E3S Web of Conferences (Vol. 507, p. 01078). EDP Sciences. https://doi.org/10.1051/e3sconf/202450701078 [20] Kollu, P.K., Bangare, M.L., Hari Prasad, P.V. et al. Internet of things driven multilinear regression technique for fertilizer recommendation for precision agriculture. SN Appl. Sci. 5, 264 (2023). https://doi.org/10.1007/s42452-023-05484-8 [21] S. M. PANDE, P. K. RAMESH, A. ANMOL, B. R. AISHWARYA, K. ROHILLA and K. SHAURYA, "Crop Recommender System Using Machine Learning Approach," 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2021, pp. 1066-1071, DOI: 10.1109/ICCMC51019.2021.9418351 [22] Kaggle Crop Recommendation Dataset. 2023. https://www.kaggle.com/datasets/atharvaingle/crop-recommendation- dataset. [23] UCI Machine Learning Repository: Agricultural Datasets. 2023. https:// archive.ics.uci.edu/datasets. [24] FAO, “Soil maps and databases,” FAO Soils Portal Data Hub, 2023. [Online]. Available: https://www.fao.org/soils-portal/data-hub/soil- maps-and-databases/en/.

DOI: https://doi.org/10.64971/j.cph.ijsdip.v13.i4.1.2025