# An Image Edge Detection Algorithm Based on an Artificial Plant Community
## 1 Introduction  
Image edge detection is an important topic in feature extraction and computer vision. It can greatly reduce the amount of data, eliminate irrelevant information, and retain the important structural attributes of an image. Image edge detection has been widely used in many areas, including spatial wave measurement [1], shape identification [2], prostate ultrasound image analysis [3], license plate recognition [4], COVID-19 detection [5], real-time moving ship detection [6,7], facial recognition [8], weak and small target infrared detection [9,10], three-dimensional microstructure reconstructions and visualizations [11], and low-light image enhancement [12]. The edge may be related to the angle of view and can vary with it. For example, different scenes and object geometries will block other objects from different angles of view. The edge may also be independent of the angle ofview, depending on the properties of the object being observed, such as the surface texture and surface shape [2; 13]. However, the edge of the natural image is not always the ideal ladder edge. On the contrary, it is usually affected by one or more factors [9], including focus blur caused by a limited scene depth, penumbra blur caused by shadows generated by nonzero radius light sources, shadows on the edges of smooth objects, and the local specular or the diffuse reflections near the edges of objects. In edge detection, it is necessary to quickly search for a set of pixels with sharp changes in the surrounding pixel gray level from amongst a large number of image pixels, which is not an easy task. The edge is the most basic feature of an image. It exists between the target, background, and region [14] and is the most important basis for image segmentation. Due to the difficulty of the edge detection task, many researchers have developed algorithms to improve the solving performance. Some heuristic and artificial intelligence(AI) algorithms have been widely applied in image edge detection and image processing in recent years, such as the artificial neural network (ANN) [11], the U-net [1], the depth-perceptual network [3], the long short-term memory (LSTM) [5], the edge-net [7], the Y-net [12], the convolutional neural networks (CNNs) [10; 11; 12], deep learning (DL) [13; 14; 15], machine learning [16], fuzzy logic [8; 10], particle swarm optimization (PSO) [14], ant colony optimization (ACO) [15; 17], the genetic algorithm (GA) [18], the support vector machine (SVM) [18], the artificial bee colony (ABC) [5], the artificial fish swarm algorithm (AFSA) [19], the salp swarm algorithm (SSA) [20], bird swarm optimization (BSO) [21], and simulated annealing (SA) [22]. In these algorithms, each individual in the group has only simple intelligence, and complex intelligent behaviors are shown through cooperation with each other. In image edge detection, the core of swarm intelligence is that a group composed of many simple individuals can achieve a certain function and perform the edge detection task together, through simple cooperation with each other. However, the accuracy and speed of the algorithm are often in conflict. Complex algorithms often have high accuracy in image segmentation, but they take too long. Most artificial intelligence algorithms focus on the behaviors of animals with nerves, i.e., ANN [15], CNN [14; 15; 16; 17], DL [17; 18; 19], PSO [14], ACO [19; 21], ABC [5], GA [22], AFSA [23], SSA [24], BSO [25], and naked mole-rat algorithm(NMRA) [26], but few people pay attention to the behaviors of plants. Therefore, we reviewed recent research on biological intelligence. After billions of years of evolution, plant communities can survive on the earth through their own survival strategies. Although the members of the plant community have no nerves, they can find areas rich in water and nutrients through the continuous cycle of seeding, growing, and fruiting, allowing them to survive in these areas [27; 28].