Adaptive Image Segmentation using a Fuzzy Neural Network and Genetic Algorithm for Weed Detection

Abstract

Digital images of the soybean and three species of weeds with different backgrounds were acquired for image processing. An adaptive neuro-fuzzy inference system (ANFIS) from MATLAB® 6.1 was used to obtain a preliminary separation of plant and background. To train the fuzzy inference system (FIS), red-green-blue (RGB) values were first converted to HSV color space (hue, saturation, value), and samples from plant and background were visually selected from the images to be used as inputs. These were then mapped to a binary output that indicated the presence or absence of plant. Subtractive clustering was used to generate the FIS with a total of 100 epochs. Three clusters, and therefore three rules, were found by the ANFIS. This resulted in a total of 9 membership functions. Source code for a genetic algorithm was written using the toolbox of MATLAB® 6.1 to adjust the membership functions to reduce misclassification and improve segmentation. The membership function parameters were used to generate chromosome genes. The initial population of 50 chromosomes was generated using 30% noise perturbation of the membership functions originally given by ANFIS. A final segmentation of background and plant was achieved after 10 generations. Between evaluations crossover and mutations were applied. The results showed that the adaptive neuro-fuzzy inference system and genetic algorithm segmentation system was capable of eliminating areas that were misclassified as plant and also generally the final segmentation.