Öztürk, ŞabanAkdemir, Bayram2020-03-262020-03-2620179.78154E+12https://dx.doi.org/10.1109/ELECTRONICS.2017.7995228https://hdl.handle.net/20.500.12395/3574021st International Conference on Electronics -- 19 June 2017 through 21 June 2017 -- 129782This study proposes a hybrid neural network model for the segmentation of leaf images with various illumination conditions. Segmentation of images with different illumination conditions is a quite challenging process. In particular, the shadows and dark regions in the image can be quite misleading for traditional segmentation algorithms. Using a single feature or reviewing them in a single colour space may work for some images, but this approach does not work on the entire dataset that have different colour. For this reason, automatic segmentation method is proposed in this study by using components from four different colour spaces. Firstly, the image is converted into RGB, HSV, XYZ and YIQ channels. Then, B, S, Z and I components are used to train hybrid neural network. Grey wolf optimizer is used for neural network optimization. The segmentation results of proposed method are compared with the well-known segmentation algorithms and are more successful. The results of proposed method are that sensitivity is 99.66 %, specificity is 98.42 % and accuracy is 99.31 %. © 2017 IEEE.en10.1109/ELECTRONICS.2017.7995228info:eu-repo/semantics/closedAccessgrey Wolf optimizerhybrid neural networkillumination conditionLeaf segmentationConference ObjectN/A