<![CDATA[An Augmented Reality application for detecting pests and diseases in rice and corn has been developed to overcome the limitations of visual identification in the field, which still relies on subjective interpretation by users. This system utilises image processing and AR overlay based on smart farming to classify symptoms in real time, improving the precision of diagnosis and consistency in control decision-making. This study aims to design, implement, and evaluate the performance of an augmented reality (AR)-based smart farming system for the visual and interactive detection of pests and diseases in rice and corn crops. The research method uses an evaluative approach by assessing the performance of the Augmented Reality system in the field based on detection accuracy, operational reliability, and the suitability of the results to the predetermined performance indicators. Testing was conducted in Gampong Releut Barat, Dewantara District, North Aceh. The results showed that pest and disease detection accuracy increased from 42.4% to 66.7%, with a system response time of <2 seconds, accompanied by an 18% reduction in crop damage and a 24% increase in productivity, confirming the reliability of the system for field diagnosis. This achievement is significant because it meets the operational performance threshold for smart farming and demonstrates the system's readiness for adoption as an Augmented Reality-based decision support tool at the farmer level. The research conclusion indicates that Augmented Reality-based smart farming has the potential to improve detection accuracy, control efficiency, and crop productivity as a support for precision agriculture and sustainable village food security.]]>
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