Machine Learning–Based Fertilizer Recommendation System using Soil and Climate Parameters
DOI:
https://doi.org/10.15662/IJEETR.2026.0802024Keywords:
Fertilizer Recommendation System, Machine Learning, Soil and Climate Analysis, Cat BoostAbstract
This project creates a smart crop and fertilizer recommendations system that uses soil properties such as NPK values and pH, as well as climate factors such as rainfall and temperature, to support agricultural decision making. The system will use Boruta and Recursive Feature Elimination to identify important features of the environment, while also using Cat Boost-based classification and regression models to predict the best crop type, fertilizer type and amount needed. A system of expert knowledge is also embedded to detail efforts on nutrient guidance and application timing. The solution will be deployed via a Stream lit application, where farmers can upload soil and crop data in a batch format and get interactive data-driven recommendations. By considering environmental inputs, machine learning predictions, and expert knowledge, the system seeks to encourage sustainable farming practices, appropriate fertilizer use, and improved crop productivity.
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