# An Image Edge Detection Algorithm Based on an Artificial Plant Community
## 5 Experimental Analysis  
### Experiment Results  
In this section, we provide an experimental analysis of the proposed APC algorithm on edge detection problems. The experimental results, with different simulation parameters and detection effects, are further compared with other related algorithms to illustrate the application and characteristics of the APC. In these experiments, our algorithm uses several plant individuals to search for edges and nonedges, and the population size of the plant community may determine its searching capability. Three learning probabilities are used to prevent the artificial plant community from prematurely falling into local optimal solutions. Some assumptions are made before the experimental analysis. Figure 2: The APC algorithm for image edge detection. 1. The structural complexity, topic, politics, history, ethics, ecology, and privacy protection factors of images are not considered. 2. The performance differences between the binarization methods and the filtering algorithms are not considered and the color differences of pixels are not compared. 3. It is assumed that different algorithms run the same iteration steps for the same images with the same parameters. 4. To simplify the analysis, we do not discuss comprehensive parameter adjustment and optimal data sets. Many algorithms have complicated improved editions, which may allow better effects on different test datasets through intelligent parameter adjustment; however, these are not studied here. The experimental platform includes an AMD Ryzen 3 4300U with Radeon Graphics 2.70 GHz CPU, 8.00 GB RAM, a 64-bit Windows 10 operating system, and Matlab R2018a simulation software. The main simulation parameters used in our proposed algorithm are as follows: The population size of the artificial plant community is \(S=20\). For the three learning factors, the seeding probability is \(p_{s}=0.9\), the growing probability is set as \(p_{g}=0.8\), and the fruiting probability is \(p_{f}=0.7\). The maximum number of iterations is preset to 120, and the error threshold is less than 0.0001. There are many test sets for image processing, so they could not all be selected for our tests. The Pascal VOC dataset and Stanford background dataset were chosen for our test, and were sufficient to verify the application of our proposed method. We provide the links to the two supporting datasets in the Data Availability Statement. The Pascal VOC 2012 segmentation competition dataset is the ancestor of visual recognition competitions, and provides 20 categories of objects to be identified, such as people, animals, vehicles, and indoor scenes. It includes target detection, object classification, image segmentation, and other tasks. Figure 3 shows our edge detection solutions on four images from the Pascal VOC 2012 dataset. The four images have 500 \(\times\) 375 pixels, 356 \(\times\) 480 pixels, 500 \(\times\) 375 pixels, and 314 \(\times\) 186 pixels, respectively. The first column in Figure 3 contains the original images, and the second column shows the histograms. Columns 3 to 8 show us the solving processes used by the artificial plant community. In the early solving stage, the artificial plant community can easily determine the edges with habitable values higher than the high threshold. The plant community will then further search for possible edge pixels to connect the image into contours. In the different test images shown in Figure 3, the artificial plant community can successfully build optimal edge images by feeding, growing, and fruiting, as shown  
Figure 3: The test results of the proposed algorithm on the Pascal VOC dataset. from iterations 20 to 120 in Figure 3. In every test, the solutions for the same image may be different, because the evolution behavior of the artificial plant community is random and the pixel searching processes will also vary.