2571-1075ISSN (Online)
Volume 10 , Issue 1 , PP: 26-34, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
K. Dhineshkumar 1 * , Tatiraju V. Rajani Kanth 2 , A. Babiyola 3 , Haritima Mishra 4
Doi: https://doi.org/10.54216/IJBES.100104
Smart agriculture leverages Internet of Things (IoT) technology to improve crop yield, resource efficiency, and environmental sustainability. This study presents an IoT-based smart agricultural monitoring system that integrates Wireless Sensor Networks (WSNs) with predictive analytics to monitor key environmental parameters, such as soil moisture, temperature, humidity, and light intensity, in real-time. The system utilizes WSNs to gather data from distributed sensor nodes and employs machine learning models for predictive analytics, enabling proactive decision-making for optimized irrigation, fertilization, and pest control. Experimental results demonstrate that the proposed system enhances resource usage by 40% and increases crop yield by 30% compared to traditional farming methods with Artificial Intelligence (AI). Additionally, the predictive analytics component achieves an accuracy of 92% in forecasting environmental conditions, aiding in timely interventions and minimizing crop stress. This IoT-based solution supports sustainable farming practices and offers scalability for various agricultural applications, including precision farming and greenhouse monitoring.
IoT, Smart Agriculture , Wireless Sensor Networks (WSN) , Predictive Analytics , Real-Time Monitoring , Soil Moisture , Resource Optimization , Crop Yield , Sustainable Farming , Precision Agriculture , Greenhouse Monitoring, Artificial Intelligence (AI)
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