RESEARCH ON WEED RECOGNITION AND CROP ROW EXTRACTION TECHNOLOGY BASED ON DEEP LEARNING

To address the limitation of existing agricultural unmanned plant protection equipment in perceiving crop growth status in real time during the maize seedling stage, this study proposes a crop row extraction method based on image processing. A crop semantic segmentation network was developed using the UNet framework, with VGG19 as the encoder and transposed convolution as the decoder. Model testing demonstrated that the segmentation network achieved accuracy rates of 0.9865 on the training set and 0.9864 on the validation set, with corresponding loss values of 0.0254 and 0.0270. In continuous processing scenarios, the average time for semantic segmentation per image was 120 milliseconds, while crop row extraction required 23 milliseconds.