Copyright © 2025 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{175818,
        author = {Praveen Eswar M and Gayathridevi M and Elamaran G and Vignesh S},
        title = {Smart Farming Assistant},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3906-3909},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175818},
        abstract = {The Smart Farming Assistant is an integrated system designed to support farmers with intelligent and data-driven agricultural decisions. It comprises multiple modules including crop recommendation based on soil conditions, fertilizer suggestion using machine learning (XGBoost), a detailed farming guide, plant disease detection using a CNN model trained on the PlantVillage dataset, and a location-based recommendation system that provides real-time weather and soil data. By combining AI, machine learning, and real-time data analysis, the assistant aims to increase productivity, reduce crop failure, and promote sustainable farming practices tailored to local conditions.},
        keywords = {Smart Farming, Crop Recommendation, Fertilizer Prediction, Plant Disease Detection, Machine Learning, XGBoost, CNN, Precision Agriculture, Sustainable Farming, Streamlit Interface.},
        month = {April},
        }