A Machine Learning-Powered Framework for Predictive Soil Analysis and Smart Fertilizer Recommendation
- Version
- Download 9
- File Size 368.82 KB
- File Count 1
- Create Date 22 March 2026
- Last Updated 22 March 2026
A Machine Learning-Powered Framework for Predictive Soil Analysis and Smart Fertilizer Recommendation
S. Venkatesh
Department of Computer Science and Engineering Dr. M.G.R. Educational and Research Institute Chennai,India
venkatmcc007@gmail.com
Dr. Saravanan Elumalai
Department of Computer Science and Engineering Dr. M.G.R. Educational and Research Institute Chennai, India
saravanan.e@drmgrdu.ac.in
V. Muthu Krishnan
Department of Computer Science and Engineering Dr. M.G.R. Educational and Research Institute Chennai,India
vmkrishnan08@gmail.com
Dr. M. Sujitha
Department of Computer Science and Engineering Dr. M.G.R. Educational and Research Institute Chennai, India
sujitha.cse@drmgrdu.ac.in
Dr. V. Sai Shanmuga Raja
Department of Computer Science and Engineering Dr. M.G.R. Educational and Research Institute Chennai, India
saishanmugaraja.cse@drmgrdu.ac.in
Abstract—In contemporary agriculture, optimizing soil health anddelivering precise fertilizer guidance are indispensable to enhancing crop yield, sustainability, and resource efficiency. This paperpresents AGROMIND, an intelligent Machine Learning-Powered Framework for Predictive Soil Analysis and Smart FertilizerRecommendation, designed to transform conventional soil management practices. The framework employs machine learningalgorithms to evaluate key soil parameters including pH, nitrogen (N), phosphorus (P), potassium (K), moisture, and temperature.Based on these parameters, the system carries out two primary functions: it evaluates soil quality to suggest necessary correctivemeasures through targeted fertilizer usage, and it identifies the most appropriate fertilizer type for specific regional soil conditions. By training predictive models on varied soil datasets, the system delivers accurate, data-driven fertilizer guidance that encourages sustainablefarming. The solution is deployed via the Django web framework,providing farmers and agricultural specialists an accessible andintuitive interface for enhanced soil management and cropproduction decision-making.
Keywords—soil health analysis; machine learning; fertilizerrecommendation; soil salinity prediction; soil parameter classification; sustainable agriculture; Django web deployment; supervised learning algorithms
Download