<![CDATA[Coffee is one of Indonesia's main commodities and an important agricultural sector for the economy. However, one of the challenges in coffee cultivation is disease. Improper and delayed treatment can cause leaf damage and death of coffee plants. This study aims to detect disease types on coffee leaves using a deep learning CNN approach and lightweight CNN architectures such as MobileNet and EfficientNet variants. This study also applies traditional image augmentation and OpenCV. The results of EfficientNetV2S and EfficientNetV2L achieve 95–98% accuracy with stable precision and recall in almost all classes, although minority classes remain challenging. The MobileNetV3 architecture showed optimal results with 99% accuracy in all variants (Small, Large, and Small with OpenCV augmentation). The research model was verified using local coffee leaf images Bumi Pajo. These findings confirm that MobileNetV3 not only excels in terms of accuracy but also has the potential to be applied to mobile device-based or Internet of Things (IoT) coffee leaf disease monitoring systems. With high accuracy and low computational requirements, this model can support real-time disease detection in the field, helping farmers and agricultural practitioners make quick and accurate decisions in disease control.]]>
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