Abstract:

As for the weeds image recognition model, the GSOFM spacial clustering model is developed especially for the shortage that segmentation threshold should be selected on the weeds image segmentation by threshold segmentation, which is combined with the segmentation algorithm of super green features and the SOFM network. This method is an unsupervised learning way without a specified threshold, which realizes the weeds image segmentation via the network’s characteristics of selforganization and competition. However, SOFM spatial clustering is achieved through the two eigenvectors of gray scale and normalization in the super green feature after processing.The experimental results show that the segmentation results have got some certain improvement with the improved GSOFM method, compared with other three kinds of weeds image segmentation algorithms, rising by 25%, 30% and 28% respectively than the HIS threshold segmentation, the super green characteristics segmentation and the double thresholds segmentation. The identification accuracy can reach 94% with this algorithm after denoising which is combined with morphology in the later phase.

Key words: SOFM;weed identification;image segmentation;extragreen feature