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Smart AGRO Advisors

Authors

Dr. R. Madhavi

Keshav Memorial Engineering College (Affiliated to Osmania University) Hyderabad, India (IN)

Uppara Vishruth

Keshav Memorial Engineering College (Affiliated to Osmania University) Hyderabad, India (IN)

Edla Pradeep

Keshav Memorial Engineering College (Affiliated to Osmania University) Hyderabad, India (IN)

Martha Narender

Keshav Memorial Engineering College (Affiliated to Osmania University) Hyderabad, India (IN)

Article Information

DOI: 10.51583/IJLTEMAS.2025.1406000115

Subject Category: Engineering

Volume/Issue: 14/6 | Page No: 1051-1059

Publication Timeline

Submitted: 2025-07-24

Published: 2025-07-24

Abstract

Abstract—Excessive and improper use of fertilizers leads to soil degradation and reduced agricultural productivity, negatively impacting farmers’ income. Description: Create a data-driven solution that recommends optimal fertilizer types and quantities based on soil health, crop type, and weather patterns, ensuring sustainable agricultural practices. Expected Solution: An application that analyzes soil data and provides tailored fertilizer recommendations, promoting sustainable farming while enhancing crop yield and farmer income. The implemented model achieves high accuracy and offers an intuitive interface for farmers, ensuring ease of use and effective adoption.

Keywords

Random Forest, fertilizer recommendation, crop recommendation, Farmer-Friendly Interface, Precision Agriculture

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References

1. G Sharma, V., Kumar, N. and Rao, L. (2022) 'Big data and ML applications in farming', International Journal of Agricultural Data Science, 16(4), pp. 233– 249. [Google Scholar] [Crossref]

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3. Wilson, R., George, M. and Patel, S. (2020) 'Comparing traditional and ML-based crop prediction methods', AI & Agriculture Review, 9(1), pp. 101– 119. [Google Scholar] [Crossref]

4. Das, P., Mehta, K. and Roy, S. (2023) 'Smart irrigation management using ML models', Journal of Precision Agriculture, 20(3), pp. 289–307. [Google Scholar] [Crossref]

5. Miller, J., Wang, T. and Chang, H. (2022) 'Automating yield prediction with AI techniques', Computational Agriculture & AI, 19(4), pp. 199–216. [Google Scholar] [Crossref]

6. Fernando, R., Li, J. and Chen, T. (2021) 'Remote sensing and ML for crop monitoring', IEEE Sensors Journal, 33(5), pp. 90-104. [Google Scholar] [Crossref]

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