Early disease detection in greenhouses is an important part of integrated disease management in modern agriculture. A real-time object detection method of melon leaf disease, Pruned-YOLO v5s+Shuffle (PYSS) is proposed in this research. First, for enhancing the feature extraction capability, the backbone of the YOLO v5s is reconstructed with ShuffleNet v2 Inverted Residual block. Then, to further downsize the model, the channel pruning method is used to prune and fine-tune the sparsely trained model. Finally, Pruned-YOLO v5s+Shuffle model is deployed to Jetson Nano, and the real-time performance is confirmed in melon greenhouses. The experimental results show that the proposed model has 93.2% and 98.2% mAP@0.5 for melon (Cucumis melon. L) powdery mildew and melon real leaves, respectively. Compared with YOLO v5s, the performance of our proposed model is improved 6.2% and 6.4% in the term of mAP@0.5 and precision, respectively. The model size and inference time are reduced 85% and 7.5%. In addition, the PYSS demonstrates the higher detection precision and faster inference speed in the comparison of YOLO v3, Faster R-CNN, RetinaNet, Cascade R-CNN, YOLO v4 and YOLO v5s. Being deployed to Jetson Nano, the detection results are displayed on the monitor in real time: mAP@0.5 is 96.7%, the model size is 1.1 MB, and the inference time is 13.8 ms.

温室病害早期检测是现代农业病害综合管理的重要组成部分。本研究提出了一种瓜叶病实时目标检测方法Pruned-YOLO v5s+Shuffle (PYSS)。首先，为了增强特征提取能力，YOLO v5s 的主干使用 ShuffleNet v2 Inverted Residual 块进行重构。然后，为了进一步缩小模型，使用通道剪枝方法对稀疏训练的模型进行剪枝和微调。最后将Pruned-YOLO v5s+Shuffle模型部署到Jetson Nano上，在瓜类大棚中验证了实时性。实验结果表明，该模型对甜瓜（Cucumis melon.L)白粉病和甜瓜真叶分别。与 YOLO v5s 相比，我们提出的模型在 mAP@0.5 和精度方面的性能分别提高了 6.2% 和 6.4%。模型大小和推理时间减少了 85% 和 7.5%。此外，在 YOLO v3、Faster R-CNN、RetinaNet、Cascade R-CNN、YOLO v4 和 YOLO v5s 的比较中，PYSS 展示了更高的检测精度和更快的推理速度。部署到Jetson Nano，检测结果实时显示在显示器上：mAP@0.5为96.7%，模型大小为1.1 MB，推理时间为13.8 ms。