Quickly and accurately obtaining information on the area of special plant planting plots is of great significance for drug production estimation and prevention of drug criminal activities. Aiming at the problem that existing special plant planting plot detection algorithms in high-resolution remote sensing images cannot quickly obtain location information and area information at the same time， this paper proposes an improved PSPNet semantic segmentation model suitable for quickly and accurately extracting certain special plant planting plots. . By introducing the channel attention SE module， the problem of holes in the segmentation of a certain special plant planting plot is solved. The Dice Loss loss function is added to improve the problem of imbalance of positive and negative samples. The encoder-decoder structure is introduced to make the extracted special plant planting Lot outline boundaries are more precise. By using the MobileNetv2 backbone network， the model prediction speed is increased by 90%. The improved I-PSPNet model achieved 95% and 84% MPA and 84% MIoU in the extraction of a special plant planting plot， and the detection efficiency reached 84 fps. Comparative experiments between I-PSPNet and UNet， Deeplabv3+， and PSPNet show that the prediction accuracy and speed of the improved model are better than the above three models. Among them， MPA increased by 24%， 7.4%， and 7.7%， and MIoU increased by 24%， 7.4%， and 7.7%. 19%， 4.3% and 4.9%， predicted speed improvements of 57 fps， 56 fps and 40 fps. At the same time， the improved model also has good applicability to RGB band data sets and GF-2 images. The improved model proposed in this article can be used to quickly and accurately obtain the location information and area information of a special plant planting plot， and help the anti-drug department quickly discover the illegal planting of a special plant planting plot， objectively assess the scale of illegal planting， and implement precise crackdowns on illegal drug and criminal activities. Provide technical support.