Data-Driven Smart Agriculture using IoT and Machine Learning
DOI:
https://doi.org/10.15662/IJEETR.2026.0802365Keywords:
Smart agriculture, Internet of Things, machine learning, precision farming, Data analytics, irrigation optimization, crop monitoring, sustainable agricultureAbstract
The advancement of precision agriculture has enabled the adoption of data-driven technologies to improve crop productivity and efficient resource utilization. This paper presents a smart agriculture system that integrates Internet of Things (IoT) devices with machine learning techniques to support intelligent decision-making in farming practices. Sensors are deployed to continuously monitor environmental and soil parameters such as moisture, temperature, humidity, and light intensity, and the collected data is transmitted to a centralized platform for analysis. Machine learning algorithms are used to identify patterns, predict crop requirements, optimize irrigation scheduling, and detect early signs of plant diseases.
The system also enables automation and remote monitoring, reducing manual intervention and operational costs. By minimizing water consumption and excessive use of fertilizers, the proposed approach promotes sustainable farming practices
Experimental results indicate that the system enhances crop yield, improves efficiency, and provides a scalable solution to address modern agricultural challenges
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