Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{193852,
        author = {Shravani.S.Bhosale and Sakshi.H.Ballal and Rutuja.D.Desai and Viraj.D.Deshmukh},
        title = {Agri Forecast: A Review of Machine Learning Approaches for Crop Recommendation, Yield Prediction, Price Forecasting, and Fertilizer Recommendation.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1951-1960},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193852},
        abstract = {Agriculture plays a crucial role in the economy of developing countries like India and others. Farmers often face difficulties in selecting the appropriate crop, determining the required fertilizer, estimating crop yield, and predicting market prices. These uncertainties lead to financial loss and reduced agricultural productivity. To overcome these issues, intelligent decision-support systems based on machine learning can be utilized.
This research proposes Agri-Forecast, a machine learning-based agricultural prediction system that integrates four major prediction models: Crop Recommendation, Fertilizer Recommendation, Crop Yield Prediction, and Crop Price Prediction. The system analyzes soil nutrients, environmental parameters, and historical agricultural data to provide accurate insights to farmers. Machine learning algorithms such as Decision Tree, Random Forest, Support Vector Machine and XGBoost are applied to develop predictive models. These models help farmers select suitable crops, apply the correct fertilizers, estimate crop production, and understand market price trends.
The proposed system aims to improve agricultural productivity, reduce risk, and support farmers in making data-driven farming decisions.},
        keywords = {Smart Agriculture, Machine Learning in Agriculture, Crop Recommendation System, Fertilizer Recommendation Model, Crop Yield Prediction, Agricultural Price Forecasting, Precision Farming, Agricultural Data Analytics, Soil Nutrient Analysis, Agricultural Decision Support System, Sustainable Farming Technology},
        month = {March},
        }