The corn silk is the pollinating organ of maize, and its growth and development are closely related to the yield. In order to realize the automatic monitoring and evaluation of corn silk characters, proposing a refined extraction method of corn silk based on adaptive trimap. First, the significance detection algorithm based on histogram contrast is used to estimate the significance of the target and form a saliency image. Then, the saliency image is preliminarily pre segmented by using adaptive threshold segmentation, and finding the largest general domain to obtain a single target binary image. According to the characteristics of the silk, determining the best convolution kernel for morphological processing, and generating a trimap containing the background and uncertain regions. Laplace matrix is used to optimize the block matrix of the objective function in the closed form matching algorithm. Finally, according to the characteristics of the corn silk, the soft segmentation of the corn silk is carried out by combining the results of the trimap and the optimized algorithm to achieve refined extraction. The pixel accuracy value of this research method is 97.96%, and the comprehensive evaluation index F-measure value is 94.16%. Compared with OTSU algorithm and Grabcut algorithm based on point operation, and deep learning algorithms SeFormer and DeeplabV3 based on neural network, the accuracy is increased by 10.43, 2.35, 1.67 and 1.91 percentage points respectively, and the F-measure value is increased by 23.96, 5.59, 4.28 and 5.13 percentage points respectively. This method can effectively remedy the defect of hard segmentation algorithm in the segmentation of special objects such as corn ear silk. The extracted target area is close to its real area with high accuracy, thus providing technical support for intelligent monitoring of corn silk growth.