• Farheen Siddiqui
• Shilpi Shukla
• Dr. Yusuf Perwej
• Sweta Singh
• Dr. Nikhat Akhtar

Farmers need to get crop recommendations to boost their production and profits. But conventional approaches for recommending crops sometimes rely on heuristic principles or expert knowledge, which may not be precise or responsive to changing environmental and market circumstances. So, there has to be a data-driven method that can use the knowledge about soil, weather, and crops that is already accessible to provide farmers the best crop options. In this research, we provide a cohesive methodology that employs IoT data and machine learning models to improve precision agriculture. We gathered a large secondary dataset from an internet data source that included environmental factors including temperature, humidity, and soil nutrient levels from several sensors put up in agricultural areas. The data were cleaned up and utilized to develop algorithms that could estimate crop production and provide advice. Our assessment indicates that the Decision Tree, Light GBM, and Random Forest classifiers attained high accuracy rates of 98.2%, 98.3%, and 99.31%, respectively. The IoT data gathering made it possible to monitor things in real time and enter data accurately, which greatly improved the models' performance. The results of this study might help farmers, agronomists, and politicians make better decisions about how to use resources and increase agricultural output by using data-driven insights.

Agriculture Data, Crop Recommendation, Machine Learning, Crop Selection, Pre-process, Crop Yield Prediction

"IoT-Enabled Decision Support System for Crop Recommendation Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.b803-b809, November-2025, Available :http://www.jetir.org/papers/JETIR2511200.pdf

"IoT-Enabled Decision Support System for Crop Recommendation Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. ppb803-b809, November-2025, Available at : http://www.jetir.org/papers/JETIR2511200.pdf