International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Soil Health Assessment Using Artifical Intelligence |
|---|---|
| ABSTRACT | Soil health is a critical determinant of global food security and sustainable land management. Traditional laboratory-based soil assessment methods, while accurate, are often constrained by high costs, labor intensity, and significant temporal lags. This study proposes an integrated Artificial Intelligence (AI) framework for the rapid assessment of soil physio-chemical properties using [insert data source, e.g., multispectral satellite imagery / IoT sensor arrays]. Utilizing [insert model, e.g., Random Forest or Convolutional Neural Networks], we analyzed key indicators including Nitrogen (N), Phosphorus (P), Potassium (K), pH levels, and Soil Organic Carbon (SOC). Our results demonstrate that the AI-driven approach achieved a [insert %] accuracy rate in predicting nutrient deficiencies compared to traditional ground-truth samples. The findings suggest that AI-based monitoring provides a scalable, cost-effective solution for real-time precision agriculture, enabling farmers to optimize fertilizer application and mitigate soil degradation. |
| AUTHOR | DR.P.JAMUNA RANI, B.RITHWIKA, A.PRAVALLIKA, CH. AKSHAYA, B.VENKATA SRAVANTHI, B.LAKSHMI LOWKYA Associate professor, Department of Chemistry, Mahendra Institute of Technology, Namakkal, India UG Student, Department of Computer Science and Engineering, Mahendra Institute of Technology, Namakkal, Tamil Nadu, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404016 |
| pdf/16_Soil Health Assessment Using Artifical Intelligence.pdf | |
| KEYWORDS | |
| References | 1. Awais, M., Naqvi, S. M., et al. (2023). "AI and machine learning for soil analysis: An assessment of sustainable agricultural practices." Bioresour Bioprocess, 10(1), 90. https://doi.org/10.1186/s40643-023-00710-y 2. Rahman, R., & Das, K. N. (2025). "Artificial Intelligence and Machine Learning in Soil Analysis for Precision Agriculture: A Review." Journal of Experimental Agriculture International, 47(5), 511-524. https://doi.org/10.9734/jeai/2025/v47i53440 3. Wadoux, A. M. J.-C. (2025). "Artificial intelligence in soil science." European Journal of Soil Science, 76(2), e13500. https://doi.org/10.1111/ejss.13500 4. Ding, Z., Liu, K., et al. (2024). "Advancing Soil Organic Carbon Prediction: A Comprehensive Review of Technologies, AI, Process‐Based and Hybrid Modelling Approaches." Journal of Soil Science, (Online Early Access). https://pmc.ncbi.nlm.nih.gov/articles/PMC12376622/ 5. MDPI Remote Sensing (2025). "Soil Organic Carbon Assessment Using Remote-Sensing Data and Machine Learning: A Systematic Literature Review." Remote Sensing, 17(5), 882. https://doi.org/10.3390/rs17050882 6. MDPI Agriculture (2025). "Soil Organic Carbon Monitoring and Modelling via Machine Learning Methods Using Soil and Remote Sensing Data." Agriculture, 15(9), 910. https://doi.org/10.3390/agriculture15090910 7. Minasny, B., & McBratney, A. B. (2026). "Generative artificial intelligence in soil science: From data augmentation to soil digital twins." ResearchGate, Publication 393920114. https://www.researchgate.net/publication/393920114 8. MDPI Agriculture (2025). "Emerging Trends in AI-Based Soil Contamination Monitoring and Prevention." Agriculture, 15(12), 1280. https://doi.org/10.3390/agriculture15121280 |
