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The drawbacks of traditional crop and weed identification algorithms include low accuracy, poor realtime and weak robustness，resulting in weeding operations inefficient in the natural environment. In order to solve these problems,，corn and its associated weed were taken as research object,，and an improved single shot multibox detector （SSD）model was proposed. Firstly, a light weight feature extraction unit was constructed through the use of depth separable convolution and squeezeandexcitation networks （SENet）module. On this basis, a light weight basic network formed with dense connection was adopted to replace the VGG16 network of the standard SSD model, so as to improve the speed of image feature extraction. Based on the mechanisms of different classification feature layer fusion, the deep semantic information in extra feature layers was fused with shallow detail information. The fused feature map would have enough resolution and strong semantic information, which can improve the detection accuracy of smallscale crops and weeds. Experimental results showed that the mean average precision and recognition speed of the proposed model were 88.27% and 32.26f/s, respectively, and the parameters size was 8.82×10.6. Compared with that of standard SSD model, the identification accuracy and speed of this model were increased by 2.66 percentage points and 33.86%, respectively, and the parameters were decreased by 66.21%. In addition, the improved SSD model performed good robustness ability under the condition of smallscale targets and overlapping of crop and weed leaves. The proposed method could identify crop and weed accurately and rapidly, which provided a technical support for agricultural automatic precision weeding. 