Segmentation for low depth of field crop disease images based on saliency and blurred detection

Most existing image-based methods for crop disease diagnosis usually have high requirement of the input images, including simple background, sufficient depth of field, etc.These methods always need to remove the complex background when doing image preprocessing, which lead to obtain the desired results difficultly.Besides, when the lesion areas are small, the captured micro images always have the low depth of field, which cannot be processed effectively by these methods to extract the accurate lesion areas.In order to solve these problems, the paper proposed a method which uses target detection to segment the lesion images.Firstly, by integrating the structural features and color features extraction and feature space quantization, the saliency region of crop disease images was detected.The lesion areas can be extracted without the preprocessing of removing the complex background.Meanwhile, to deal with the crop diseases images with the low depth of filed, the blurred detection was introduced to further filter the background or blurred images.The images of various diseases of cucumber and rice were used in the experiments.The experimental results showed that our method was much better than the threshold method on accuracy and much more efficient than graph cuts method on efficiency in image segmentation of the crop disease images.Meanwhile, our method can effectively extract the lesion areas from the crop disease images with the low depth of field.