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The cocoa tree is prone to diverse diseases such as stem borer, stem canker, swollen shot, and root rot disease which impedes high yield. Early disease detection is a critical component of diverse management processes that are implemented throughout the life cycle of cocoa plants. Consequently, several studies on the application of detection techniques to recognize diseases have been proposed by several researchers. This study proposes the YOLOv5m network for cocoa tree disease detection. The development of cocoa disease detection systems will aid farmers in early identification prompt response, and efficient management of related cocoa tree diseases which will ultimately increase yield and sustainability. To improve the performance of the YOLOv5m network, a Swin Transformer (Swin-T) was added to the backbone network to improve cocoa tree disease detection accuracy. By obtaining global information, the K-means++ algorithm was added to modify the choice of initial clustering locations, and Efficient Intersection over Union Loss (EIoU) was used as a bounding box regression loss function to speed up the bounding box regression rate, resulting in higher precision of the YOLOv5m network. The experimental assessment outcome of this study showed that the proposed method YOLOv5m (Swin-T, K-means++, EIoU) achieved 96% precision, mAP of 92%, and recall of 94%. Compared to the original YOLOv5m, precision improved by 5%, mAP improved by 6%, and recall by 5%. Comparing the proposed method to the conventional YOLOv5m, the latter showed improved performance and better accuracy with a high detection speed and compactness. This improvement offers a useful and effective method for detecting diseases related to cocoa trees.