辣椒病害检测对于及时采取有效的防治措施，降低因病害导致辣椒减产的风险具有重要意义，当前基于深度学习的辣椒病害检测技术尚缺，基于此，本文提出了改进Faster R-CNN的辣椒病害检测方法。采用建立辣椒病害图像集，对收集图像集进行数据增广，并标注辣椒病害样本数据集，接着对Faster R-CNN网络进行预训练，通过迁移学习机制在辣椒病害数据集上对权重参数进行微调，其网络的mAP为89.74%，可见Faster R-CNN检测辣椒病害存在漏检和效率较低的问题。本文通过采用MobileNetv2优化网络模型组合和区域候选框优化的方式对网络进行改进，将改进的Faster R-CNN网络与经典的Faster R-CNN网络的实验结果进行对比分析，结果表明：改进的Faster R-CNN检测效率显著提高，检测速度可达到10FPS，mAP达到90.01%，被漏检病害也能被网络识别检测，证实了改进的Faster R-CNN网络模型用于辣椒病害检测的可行性。

Pepper disease detection is of great significance for taking effective prevention and control measures in time and reducing the risk of pepper production due to disease. Currently, pepper disease detection technology based on deep learning is still lacking. Based on this, this paper proposed an improved Faster R-CNN pepper disease detection method. The image set of pepper disease was firstly established, then the data of the image set was augmented, and the sample data set of pepper disease was labeled. Then the Faster R-CNN network was pre-trained, and the weight parameters were fine-tuned on the pepper disease data set through the transfer learning mechanism, its mAP reached 89.74%. It can be seen that the detection of pepper diseases by Faster R-CNN has problems of missing detection and low efficiency. In this paper, the network is improved by MobileNetv2 optimization network model combination and regional candidate box optimization, the experimental results of the improved Faster R-CNN network and the classical Faster R-CNN network are compared and analyzed, the results show that: The improved Faster R-CNN detection efficiency is significantly improved, detection speed can reach 10FPS and mAP up to 90.01%, and the missed diseases could also be identified and detected by the network, confirming the feasibility of the improved Faster R-CNN network model for pepper disease detection.