AI-Driven Bio-Soil Analysis for Predictive Crop Disease Control ‎in Precision Agriculture

  • Authors

    • Aakansha Soy Assistant Professor, Department of CS & IT, Kalinga University, Raipur, India
    • Sutar Manisha Balkrishna Research Scholar, Department of CS & IT, Kalinga University, Raipur, India
    • Dr. Sushma Murlie Associate Professor, New Delhi Institute of Management, New Delhi, India
    https://doi.org/10.14419/8j7gyr88

    Received date: May 2, 2025

    Accepted date: May 29, 2025

    Published date: October 31, 2025

  • Pollution; AI-Driven; Soil; Agriculture; Integrated Circuit

  • Abstract

    This research describes the AI-Driven Bio-Soil Analysis System (AB-SAS), which utilizes metagenomic biosensors, Graph Neural ‎Networks, and Transformer models to forecast and preempt crop diseases as they arise. AB-SAS advances the measurement and ‎assessment of soil health with its Dynamic Soil Health Index (DSHI) and predictive capabilities relative to diseases based on microbial ‎diversity, nutrient stasis, and soil moisture equilibrium. The system features an AI biostimulant dispenser aimed at sustainable risk ‎mitigation. Moreover, AB-SAS meets the demands for secure, traceable, and accountable datasets and streams for system users through ‎blockchain. AB-SAS extends integrated, real-time solutions to demands for crop health maintenance, yield enhancement, and sustainable ‎crop production. The AB-SAS has incorporated plans for versatility in design, thereby extending its use in various agricultural systems. The ‎system’s geo-referenced and rapid interval approaches enable optimized resource use while avoiding exhaustion and ecological harm. This ‎Integrated AB-SAS is prospective in improving the system’s precision in agriculture and the system’s ability to promote sustainable ‎practices. The system will provide farmers with possible and immediately implementable plans and information‎.

  • References

    1. Brevik, E. C., Slaughter, L., Singh, B. R., Steffan, J. J., Collier, D., Barnhart, P., & Pereira, P. (2020). Soil and human health: current status and future needs. Air, Soil and Water Research, 13, 1178622120934441. https://doi.org/10.1177/1178622120934441.
    2. Ahmed, I., Bano, A., & Siddique, S. (2021). Morphometric and meristic characters and condition factor of Acanthopagrusarabicus (Pisces: Sparidae) from Pakistan, North Arabian Sea. Natural and engineering sciences, 6(2), 75-86. https://doi.org/10.28978/nesciences.970537.
    3. Gorliczay, E., Boczonádi, I., Kiss, N. É., Tóth, F. A., Pabar, S. A., Biró, B., ... & Tamás, J. (2021). Microbiological effectivity evaluation of new poultry farming organic waste recycling. Agriculture, 11(7), 683. https://doi.org/10.3390/agriculture11070683.
    4. Kieseberg, P., & Tjoa, S. (2021). Guest Editorial: Special Issue on the ARES-Workshops 2020. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 12(1), 1-2.
    5. Khan, A. (2024). Soil Health and Fertility: Modern Approaches to Enhancing Soil Quality. Frontiers in Agriculture, 1(2), 283-324.
    6. Radmehr, B., Ghaemi, R., &Mazinani, S. M. (2017). A Novel Intelligent Hybrid Fuzzy Method for K-Means Algorithm. International Academic Journal of Science and Engineering, 4(2), 242–247.
    7. Yang, L., & Shami, A. (2022). IoT data analytics in dynamic environments: From an automated machine learning perspective. Engineering Applica-tions of Artificial Intelligence, 116, 105366. https://doi.org/10.1016/j.engappai.2022.105366.
    8. Khyade, V. B. (2019). Efficiency of Mulberry, Morus alba (L) as fodder for cattle. International Academic Journal of Innovative Research, 6(1), 77–90. https://doi.org/10.9756/IAJIR/V6I1/1910007.
    9. Patel, P., & Dusi, P. (2023). Digital Twin Models for Predictive Farm Management in Smart Agriculture. National Journal of Smart Agriculture and Rural Innovation, 1(1), 9-16.
    10. Uken, E., & Getachew, B. (2023). IoT-Enabled Smart Aquaculture Monitoring System for Energy-Efficient Water Quality Management. National Journal of Smart Fisheries and Aquaculture Innovation, 1(1), 33-40.
    11. Surendar, A., & Reginald, P. J. (2023). Smart IoT-Enabled Hydroponic Systems for Sustainable Lettuce Production Under Controlled Environ-ments. National Journal of Plant Sciences and Smart Horticulture, 1(1), 33-40.
    12. Punam, S. R., & Patel, P. (2023). Biodiversity Corridors in Fragmented Forest Landscapes: Enhancing Connectivity for Climate-Resilient Ecosys-tems. National Journal of Forest Sustainability and Climate Change, 1(1), 17-24.
    13. Geetha, K., & Egash, D. (2023). Genomic Insights into Disease Resistance in Indigenous Cattle Breeds: Toward Sustainable Breeding Pro-grams. National Journal of Animal Health and Sustainable Livestock, 1(1), 25-32.
    14. Soy, A., & Salwadkar, M. (2023). Improving School Feeding Programs through Locally Sourced, Nutrient-Dense Foods. National Journal of Food Security and Nutritional Innovation, 1(1), 33-40.
    15. Rahim, R. (2025). Mathematical Model-Based Optimization of Thermal Performance in Heat Exchangers Using PDE-Constrained Methods. Journal of Applied Mathematical Models in Engineering, 17-25.
    16. Han, H., Liu, Z., Li, J., & Zeng, Z. (2024). Challenges in remote sensing-based climate and crop monitoring: navigating the complexities using AI. Journal of cloud computing, 13(1), 1-14. https://doi.org/10.1186/s13677-023-00583-8.
    17. Colace, F., Santo, M. D., Lombardi, M., Mosca, R., & Santaniello, D. (2020). A Multilayer Approach for Recommending Contextual Learning Paths. Journal of Internet Services and Information Security, 10(2), 91-102.
    18. Margam, R. (2024). Boosting Public Health Resilience: Harnessing Ai-Driven Predictive Analysis to Prevent Disease Outbreaks. International Jour-nal of Artificial Intelligence Research and Development (IJAIRD), 2(1), 76-90.
    19. El Jarroudi, M., Kouadio, L., Delfosse, P., Bock, C. H., Mahlein, A. K., Fettweis, X., ... &Hamdioui, S. (2024). Leveraging edge artificial intelligence for sustainable agriculture. Nature Sustainability, 7(7), 846-854. https://doi.org/10.1038/s41893-024-01352-4.
    20. Sharuddin, S. S., Ramli, N., Yusoff, M. Z. M., Muhammad, N. A. N., Ho, L. S., & Maeda, T. (2022). Advancement of meta transcriptomics towards productive agriculture and sustainable environment: a review. International Journal of Molecular Sciences, 23(7), 3737. https://doi.org/10.3390/ijms23073737.
    21. Coteur, I., Wustenberghs, H., Debruyne, L., Lauwers, L., & Marchand, F. (2020). How do current sustainability assessment tools support farmers’ strategic decision-making? Ecological Indicators, 114, 106298. https://doi.org/10.1016/j.ecolind.2020.106298.
    22. Tunlid, A., & White, D. C. (2021). Biochemical analysis of biomass, community structure, nutritional status, and metabolic activity of microbial com-munities in soil. In Soil biochemistry (pp. 229-262). CRC Press. https://doi.org/10.1201/9781003210207-7.
    23. Mishra, H., & Mishra, D. (2024). AI for Data-Driven Decision-Making in Smart Agriculture: From Field to Farm Management. In Artificial Intelli-gence Techniques in Smart Agriculture (pp. 173-193). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-5878-4_11.
    24. Nam, N. N., Do, H. D. K., Loan Trinh, K. T., & Lee, N. Y. (2023). Metagenomics: An effective approach for exploring microbial diversity and func-tions. Foods, 12(11), 2140. https://doi.org/10.3390/foods12112140.
    25. Reginald, P. J. (2025). Design of an Intelligent V2G Energy Management System with Battery-Aware Bidirectional Converter Control. National Jour-nal of Intelligent Power Systems and Technology, 1(1), 12-20.
    26. Prasath, C. A. (2025). Green Hydrogen Production via Offshore Wind Electrolysis: Techno-Economic Perspectives. National Journal of Renewable Energy Systems and Innovation, 8-17.
    27. Kumar, T. S. (2025). A Comparative Study of DTC and FOC Techniques in Multiphase Synchronous Reluctance Drives. National Journal of Electric Drives and Control Systems, 1(1), 12-22.
    28. Abdullah, D. (2025). Comparative Analysis of SIC and GAN-Based Power Converters in Renewable Energy Systems. National Journal of Electrical Machines & Power Conversion, 11-20.
    29. Surendar, A. (2025). Model Predictive Control of Bidirectional Converters in Grid-Interactive Battery Systems. Transactions on Power Electronics and Renewable Energy Systems, 13-20.
    30. Abdullah, D. (2025). Redox Flow Batteries for Long-Duration Energy Storage: Challenges and Emerging Solutions. Transactions on Energy Storage Systems and Innovation, 1(1), 9-16.

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  • How to Cite

    Soy, A. ., Balkrishna , S. M. ., & Murlie , D. S. . (2025). AI-Driven Bio-Soil Analysis for Predictive Crop Disease Control ‎in Precision Agriculture. International Journal of Basic and Applied Sciences, 14(SI-1), 338-343. https://doi.org/10.14419/8j7gyr88