Authors :
Gulam Muddasir Farooqui; Mohammed Mouzzam Mohiuddin; Syed Barkath Ali
Volume/Issue :
Volume 10 - 2025, Issue 7 - July
Google Scholar :
https://tinyurl.com/5f4nc3ac
Scribd :
https://tinyurl.com/yhjpatr2
DOI :
https://doi.org/10.38124/ijisrt/25jul1842
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Note : Google Scholar may take 30 to 40 days to display the article.
Abstract :
The Smart Farming Assistant is a machine learning-based system designed to aid farmers and agricultural planners
in making informed decisions about crop yield and market pricing. The system utilizes advanced algorithms such as
XGBoost and Random Forest to predict agricultural outcomes based on soil health, weather patterns, and historical
market data.
To ensure transparency and trust in the model’s predictions, the project incorporates SHAP (SHapley Additive
exPlanations) values, allowing users to interpret the influence of each input feature on the model’s output. This enhances
the explainability of the system, making it not only a powerful forecasting tool but also an educational aid for
understanding the relationships between environmental factors and crop performance.
The project includes a user-friendly web interface that enables users to input relevant agricultural parameters and
receive both predictions and interpretive visualizations. By combining accuracy with explainability, this Smart Farming
Assistant bridges the gap between traditional agricultural knowledge and modern artificial intelligence, promoting more
efficient and profitable farming practices.
References :
- Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine.
- Breiman, L. (2001). Random Forests.
- Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions.
- https://xgboost.readthedocs.io
- https://shap.readthedocs.io
- https://www.kaggle.com/datasets for crop/soil/weather data
- https://scikit-learn.org
- https://flask.palletsprojects.com
- https://plotly.com for visualizations
The Smart Farming Assistant is a machine learning-based system designed to aid farmers and agricultural planners
in making informed decisions about crop yield and market pricing. The system utilizes advanced algorithms such as
XGBoost and Random Forest to predict agricultural outcomes based on soil health, weather patterns, and historical
market data.
To ensure transparency and trust in the model’s predictions, the project incorporates SHAP (SHapley Additive
exPlanations) values, allowing users to interpret the influence of each input feature on the model’s output. This enhances
the explainability of the system, making it not only a powerful forecasting tool but also an educational aid for
understanding the relationships between environmental factors and crop performance.
The project includes a user-friendly web interface that enables users to input relevant agricultural parameters and
receive both predictions and interpretive visualizations. By combining accuracy with explainability, this Smart Farming
Assistant bridges the gap between traditional agricultural knowledge and modern artificial intelligence, promoting more
efficient and profitable farming practices.
