## 3 Results and Discussion
### Intensity Transformation Performance in the Third Step of Segmentation  
Figure 4 shows an image of the intensity transformation performance. Figure 4a shows the main studied image. As observed, this image has various objects such as green leaves in the shade, green leaves in the sun, soil, green plants in the shade, green plants in the sun, tiny branches, thick branches, tree trunks, and others. Figure 4b shows the image segmented in the two previous steps by color and texture methods. As observed, most of the relevant branches and trunks of trees remained without any change. Figure 4c shows the image of the intensity transformation. Eventually, by applying thresholds 95 on image Figure 4c, the image shown in Figure 4d was obtained. By comparing this image and the image of Figure 4b, it is clear that many parts of the trunk and branches have been deleted.  
The Performance of Segmentation Algorithm in Different Modes of Ordering Color, Texture, Intensity Transformation Methods  
One of the innovations of this research is to arrange the sequence of different segmentation methods. Figure 5 shows three different sequences of texture, color, and intensity transformation methods. Figure 5a shows the original image. Figure 5b shows the image segmented before applying the color thresholds with the sequence of the texture method, the color method and the intensity transformation method. As observed, the segmentation accuracy is very low, and many of the relevant apple segments have been deleted, while many background pixels are remained. Figure 5c shows the segmented image of Figure 5a with the sequence of the intensity transformation method, the texture method and the color method. This sequence of methods has better performance than the previous one, but in general, it has a low accuracy. Figure 5d shows the segmented image of the segmentation algorithm with the sequence of the color method, the texture method and the intensity transformation method. As observed, the algorithm has a very good performance. In fact, by using this sequence, a large part of the background was removed and the pixels of the apple were not deleted.  
Figure 3: Texture feature with high performance in the second segmentation process. (**a**): The original image, (**b**): The image obtained by applying the local range feature, (**c**): The image obtained by applying the local entropy feature, (**d**): The image obtained by applying the local standard deviation feature.  
Figure 4: Intensity transformation performance in the third step of segmentation. (**a**): Original image, (**b**): Image segmented before this step, (**c**): Image corresponding to intensity transformation, (**d**): Image segmented after applying the threshold on the image of the intensity transformation.  
Figure 5: The performance of segmentation algorithm in different modes of ordering color, texture, intensity transformation methods. (**a**) Original image. (**b**) The resulting image after applying the sequence of the texture method, the color method and the intensity transformation method. (**c**) The resulting image after applying the sequence of the intensity transformation method, the texture method, the color method. (**d**) The image obtained after applying the sequence of the color method, the texture method, the intensity transformation method.