IOT-Based Crop Recommendation Using Machine Learning - Sanket Chor; Bhagyashri Dandekar; Shelar Prerna; Prof. Satish Shelke; Dr. Sudhir Divekar (International Journal of Scientific Research in Science, Engineering and Technology, 2025)
This implementation report presents the design, development, and deployment of an IoT machine learning-based crop recommendation system. The system is designed to assist farmers with optimal crop recommendations made based on real-time environmental conditions reported by multiple low-cost IoT sensors. The sensors record major parameters like soil moisture, temperature, humidity, and pH levels that are sent to a cloud platform for centralized storage and processing. Sophisticated machine learning models scan present and past data to create accurate and timely crop recommendation advice in order to increase yield efficiency and support sustainable agriculture. Initial tests show enhancements in resource management and crop output, highlighting the promise of this combined solution for precision agriculture. Future efforts will be directed toward scaling the system, improving data analytics through incorporation of weather, and increasing the user interface for wider use across farming communities. Keywords: IoT-based Agriculture, Crop Recommendation System, Precision Agriculture, Machine Learning, Real-Time Data Acquisition, Sensor Networks, Cloud Computing, Environmental Monitoring, Data Preprocessing, Sustainable Farming