Web-Based Soil Health & Fertilizer Advisory System

Authors

  • K. Manjuparkavi Department of Computer Science and Engineering, R P Sarathy Institute of Technology, Salem, Tamil Nadu, India Author
  • Durga Devi V, Jerusha Karolin G, Deepana S, Barath N UG Scholars, Department of Computer Science and Engineering, R P Sarathy Institute of Technology, Salem, Tamil Nadu, India Author

DOI:

https://doi.org/10.15662/IJEETR.2026.0802068

Keywords:

Soil Health, Fertilizer Recommendation, Precision Agriculture, Web Application, Soil Nutrient Analysis, Sustainable Agriculture.

Abstract

Agricultural productivity largely depends on soil health and proper fertilizer management. However, many farmers still rely on traditional practices and may not have clear knowledge about soil nutrient conditions. This paper presents a Web-Based Soil Health and Fertilizer Advisory System that helps farmers analyze soil parameters and obtain suitable fertilizer recommendations. The system collects soil data such as Nitrogen (N), Phosphorus (P), Potassium (K), and pH values and processes the information to determine nutrient deficiencies. Based on the analysis, the system suggests appropriate fertilizers and quantities required to improve soil fertility. The system is implemented using a web-based platform that allows users to easily access the advisory service. The results show that the system can provide quick and useful recommendations, helping farmers improve crop productivity and maintain healthy soil conditions

References

[1] R. K. Ramesh and M. Kumar, “Soil Nutrient Analysis and Crop Recommendation System Using Machine Learning,” International Journal of Agricultural Technology, vol. 14, no. 3, pp. 120–126, 2021.

[2] P. Swetha and M. Lakshmi, “Crop Recommendation System Using Machine Learning Algorithms,” International Journal of Advanced Research in Computer Science, vol. 11, no. 4, pp. 45–50, 2020.

[3] M. K. Senapaty and S. Mishra, “Smart Agricultural System for Soil Fertility Analysis Using Data Mining Techniques,” International Journal of Computer Applications, vol. 178, no. 7, pp. 12–18, 2019.

[4] C. K. Shettigar and A. Rao, “Agricultural Monitoring System Based on Machine Learning and Soil Data Analysis,” IEEE International Conference on Smart Computing, pp. 210–215, 2021.

[5] S. Patel and R. Shah, “A Web-Based Decision Support System for Soil Health Management,” International Journal of Information Technology in Agriculture, vol. 9, no. 2, pp. 67–73, 2022.

[6] J. Smith and L. Brown, “Digital Agriculture: Data-Driven Approaches for Soil Fertility Management,” Journal of Agricultural Informatics, vol. 12, no. 1, pp. 34–42, 2021.

[7] J. B. Jones, Agronomic Handbook: Management of Crops, Soils, and Their Fertility, CRC Press, 2003.

[8] FAO, “Soil Fertility Management in Support of Food Security in Sub-Saharan Africa,” Food and Agriculture Organization, 2017.

[9] Brady, N. C., and Weil, R. R., The Nature and Properties of Soils, 15th ed., Pearson, 2016.

[10] R. Lal, “Soil Health and Carbon Management,” Food and Energy Security, vol. 5, no. 4, pp. 212–222, 2016.

[11] S. Basso et al., “Machine Learning for Crop Yield Prediction and Soil Analysis,” Agricultural Systems, vol. 178, 2020.

[12] M. Liakos et al., “Machine Learning in Agriculture: A Review,” Sensors, vol. 18, no. 8, 2018.

[13] A. Kamilaris and F. Prenafeta-Boldú, “Deep Learning in Agriculture: A Survey,” Computers and Electronics in Agriculture, vol. 147, 2018.

[14] S. Rajeswari and K. Arunesh, “Analysing Soil Data Using Data Mining Classification Techniques,” Indian Journal of Science and Technology, 2016.

[15] R. Sujatha et al., “Prediction of Soil Fertility Using Machine Learning Algorithms,” International Journal of Engineering Research & Technology, 2020.

[16] V. R. Patel et al., “Soil Classification Using Decision Tree Algorithm,” International Journal of Computer Applications, 2019.

[17] D. K. Sharma and A. K. Singh, “Soil Fertility Prediction Using Random Forest,” Procedia Computer Science, vol. 167, 2020.

[18] K. N. Mishra et al., “Support Vector Machine Based Soil Classification,” International Journal of Advanced Computer Science, 2018.

[19] T. Wolfert et al., “Big Data in Smart Farming,” Agricultural Systems, vol. 153, 2017.

[20] P. K. Singh et al., “Smart Agriculture System Using IoT and Machine Learning,” Journal of Cleaner Production, 2020.

[21] M. Ayaz et al., “Internet of Things in Agriculture: Applications and Challenges,” IEEE Access, vol. 7, 2019.

[22] R. K. Gupta et al., “Decision Support Systems in Agriculture,” Computers and Electronics in Agriculture, 2019.

[23] A. Bhatia and R. Dubey, “Web-Based Agricultural Advisory Systems,” International Journal of Computer Science, 2021.

[24] S. K. Balasundram et al., “Precision Agriculture Technologies,” Agronomy Journal, 2006.

[25] J. Zhang et al., “Precision Agriculture—A Worldwide Overview,” Computers and Electronics in Agriculture, 2002.

[26] S. K. Sahu et al., “Soil Health Monitoring Using IoT,” International Journal of Innovative Technology, 2021.

[27] M. K. Jha et al., “Fertilizer Recommendation System Using Data Mining,” International Journal of Agricultural Research, 2019.

[28] P. Dhawan, “Machine Learning Applications in Soil Nutrient Prediction,” Springer, 2020.

[29] S. K. Patel et al., “Crop and Fertilizer Recommendation Using AI,” Elsevier Procedia, 2021.

[30] Y. Kim et al., “Smart Farming Using Artificial Intelligence,” Sustainability, 2018.

[31] R. Pandey et al., “Soil Nutrient Detection Using Data Science,” International Journal of Scientific Research, 2020.

[32] ICAR, “Soil Health Card Scheme Guidelines,” Government of India, 2015.

USDA, “Soil Quality Assessment Methods,” United States Department of Agriculture, 2018.

[33] C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746

[33] C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2

[34] C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9

[35] S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025

[36] S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428

[37] S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w

[38] C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.

[39] C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.

[40] C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.

[41] C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007

[42] Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721

[43] M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022

[44] FAO, “Guidelines for Soil Description,” 4th Edition, 2006.

[45] K. Chlingaryan et al., “Machine Learning Approaches for Crop Yield Prediction,” Computers and Electronics in Agriculture, 2018.

[46] A. Sharma et al., “Data Mining Techniques for Soil Fertility Prediction,” International Journal of Computer Applications, 2017.

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Published

2026-03-28

How to Cite

Web-Based Soil Health & Fertilizer Advisory System. (2026). International Journal of Engineering & Extended Technologies Research (IJEETR), 8(2), 1087-1096. https://doi.org/10.15662/IJEETR.2026.0802068