Image segmentation algorithm for infrared image of pleurotus eryngii in industrialized production environment

House environment of edible fungi in industrialized production pattern greatly influences the growth of edible fungi. Workers judge the suitability of the environment in accordance with the morphological characteristics (such as mushroom stem diameter, diameter of mushroom cap, ratio of mushroom cap to mushroom stem) of edible fungi, and such judgment is often subjective and can not be judged in time due to individual differences and human factors, resulting in delay in adjustment of mushroom house environment, and affecting the yield and quality of edible fungi. In this paper, the images obtained from Pleurotus eryngii in industrialized production are targets for the study, and the improved fuzzy C-means clustering segmentation algorithm based on ant colony algorithm to obtaining morphological characteristics of Pleurotus eryngii is proposed. This algorithm can effectively conduct segmentation of image of Pleurotus eryngii obtained from production, therefore, it can meet the demand for automatic acquisition of morphological characteristics of Pleurotus eryngii.