A measuring method using image processing techniques for the rapid detection of Azotobacter chroococcum was put forward. Based on the microscopic image collection, preprocessing, segmentation and feature extraction, as well as the support vector machine (SVM) identification, classification and counting, Azotobacter chroococcum concentration density was acquired. The rapid Azotobacter chroococcum activity detection was realized. Experiments showed that detection precision of the proposed method was higher, and its relative error was less than 4% compared with precisely manual counting. Meanwhile, the detection method was more time saving. As for the other special detecting devices, the measuring detection cost was much lower.