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{177693,
        author = {Pallavi Rajendra Jadhav and Sahil Popat Ahirekar and Rohan Manoj Chaudhari and Esha Vijay Yadav and Prof. Dr.S.R. Rangari},
        title = {IoT Based Smart Soil Monitoring and Crop Recommendations using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2722-2726},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177693},
        abstract = {In the face of increasing agricultural challenges such as inefficient resource utilization, poor crop selection, and declining soil health, farmers often lack real-time, data-driven support systems to make informed decisions. This paper presents AgroSmart, an integrated solution leveraging the Internet of Things (IoT) and Machine Learning (ML) to enhance agricultural productivity and sustainability. The proposed system continuously monitors critical soil parameters using cost-effective sensors and employs machine learning algorithms to recommend the most suitable crops based on current soil conditions. Through a user-friendly dashboard, farmers gain accessible and actionable insights, bridging the gap between traditional farming practices and modern precision agriculture. Key challenges such as data quality, model accuracy, cost constraints, and user accessibility were addressed with appropriate technical and design strategies. The system demonstrates strong potential for scalability and can be effectively adapted for use in smart agriculture, greenhouses, agroforestry, and research applications. Our solution promotes sustainable agriculture by enabling optimized resource usage and improved farming efficiency, ultimately contributing to global food security and environmental resilience.},
        keywords = {IoT in Agriculture, Machine Learning, Smart Farming, Crop Recommendation System, Soil Monitoring, Precision Agriculture, Sustainable Farming, AgroSmart, Low-Cost Sensors, Data-Driven Farming.},
        month = {May},
        }