Agriculture remains crucial to many economies worldwide, yet plant diseases threaten its productivity and sustainability. Early detection and timely treatment are essential for mitigating their impact. Traditional detection methods are labor-intensive and costly, necessitating real-time solutions. Deep learning holds promise, but existing approaches struggle with simultaneous localization and classification. To address this, we propose YOLOv9++, an enhanced variant of YOLOv9 integrated with Convolutional Block Attention Module (CBAM). Our model significantly improves accuracy and efficiency in plant leaf disease detection. We introduce a large-scale leaf disease detection dataset called PlantVillage-LD2 inspired by the PlantVillage dataset. YOLOv9++ achieves competitive performance, surpassing the baseline YOLOv9, achieving a mAP:50 of 93.7% on the PlantVillage detection dataset. Moreover, on the PlantDoc dataset, YOLOv9++ achieves an mAP:50 of 61.7%, surpassing other detection algorithms. Our findings highlight the practicality of YOLOv9++ for real-time plant disease detection, fostering sustainable agricultural practices globally.