Published 2025-12-24
Keywords
- Machine Learning,
- K-nearest neighbor,
- Random Forest
How to Cite
Copyright (c) 2025 Swami Vivekananda University Press
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Abstract
Agriculture plays a leading role in the economic growth and development of the countries that make up the country. This project is full of Machine Learning algorithms in the field of agriculture to provide efficient and effective solutions to the problems faced by farmers. Some of the problems with systems that have been implemented are that they focus on a single parameter (moisture or temperature or soil pH etc.) to predict crop growth adequacy. In this project, however, many factors must be considered simultaneously to make the best and most accurate predictions for crops, such as pH, Nitrogen (N), Phosphorus (P), Potassium ( K) soil content, temperature, humidity. The proposed project provides solutions for smart farming by monitoring soil and weather characteristics that can help farmers significantly increase their productivity. This project uses machine learning algorithms such as K- Nearest Neighbors, Random Forest and a dateset with approximately 2200 rows of data. The project provided farmers with crops to grow. Therefore, it will help farmers to choose the right crops for their fields.
DOI - https://doi.org/10.65525/SVUP.9788199651517.2026.174-187