Abstract: Accurate identification and classification of pests under intelligent insect monitoring and reporting lights are the prerequisite for realizing early warning of rice insect situation. In order to solve the problems in image recognition of rice pests, such as dense distribution, small body size and susceptibility to background interference, the recognition accuracy is not high.A lightweight MS-YOLO v7（Multi-Scale-YOLO v7） based classification method for rice fly identification was proposed.Firstly, a rice planthopper pest collection platform was built with a migratory pest trapping device, and the images of rice planthopper were obtained to form the ImageNet dataset. Then the MS-YOLO v7 object detection algorithm used GhostConv lightweight convolution as the backbone network to reduce the number of parameters for model operation. CBAM attention mechanism module was added to Neck to effectively emphasize the highly differentiated feature channels of rice planthopper, suppress redundant and useless features, accurately extract key features of rice planthopper images, and dynamically adjust the weights of different channels in the feature map. SPPCSPS spatial pyramid modulewas replaced by SPPFS pyramid module to improve the feature extraction ability of the network model. At the same time, SiLU activation function was replaced by Mish activation function in YOLO v7 model to enhance the nonlinear ability of the network. The test results showed that the mean average precision（mAP）, precision（96.4%） and recall（94.2%） of the improved MS-YOLO v7 on the test set were 95.7%, 96.4% and 94.2%, respectively.Compared with that of Faster R-CNN, SSD, YOLO v5 and YOLO v7 network models, mAP was improved by 2.1 percentage points, 3.4 percentage points, 2.3 percentage points and 1.6 percentage points, respectively, and the balance score F1 was improved by 2.7 percentage points, 4.1 percentage points, 2.5 percentage points and 1.4 percentage points, respectively.The memory occupation, number of parameters, and number of floating-point operations of the improved model were 63.7 MB, 2.85×10~7, and 7.84×10¹⁰, respectively, which were scaled down by 12.5%, 21.7%, and 25.4% compared with that of the YOLO v7 model. The MS-YOLO v7 network model can realize high-precision identification and classification of interspecific pests of rice fly, with good robustness, and it can be used to realize the technical support for early warning of rice fly pest in paddy fields.