Abstract: Cotton canopy coverage is an important index for monitoring cotton growing in field. It is easy but not accurate to calculate, because it is difficult to accurately segment the cotton canopy in the complex environment image of cotton field. This paper presents an adaptive threshold segmentation approach of cotton canopy image based on logistic algorithm in order to improve the segmentation precision and robustness for cotton canopy image. Firstly, the cotton canopy image is transformed into HSV (hue, saturation, value) color space. This color space is designed by human color description. In this color space, the color feature of the pixel can be expressed by 3 independent components i.e. H, S and V. In this paper, the logistic regression algorithm is used to compute threshold used in image segmentation. The logistic regression algorithm is often used in 2 kinds of classification problem, so our method need an artificially defined variable. This variable and a single color feature variable can form a dataset as the input of logical regression algorithm to calculate the segmentation threshold. In our paper, the proposed artificially defined variable is set to a specific value that is 1, the effect of which is to reduce the impact for computed segmentation threshold. The cotton canopy image's pixel is divided into 2 classes: target and background. The H channel feature of 2 classes can be extracted in HSV color space, and the green ratio (G/(R+G+B)) of 2 classes can be extracted in RGB (red, green, blue) color space. Those features' thresholds are computed by logistic regression algorithm. H channel thresholds are used to achieve the first segmentation. Secondly, the first segmentation result is divided to highlight pixels and low pixels. The highlight pixels mainly include light canopy and light soil, and the low pixels mainly include shadow canopy and shadow soil. However, it is difficult to segment cotton canopy in the low pixels. In order to solve this problem, extra-green (Exg) color feature is used as segmentation feature to get cotton canopy in the low pixels. Thirdly, the highlight pixels in the first segmentation result and the low pixels segmented by Exg threshold are segmented by green ratio threshold. This segmentation is called the second segmentation. At last, the segmentation result of cotton canopy is acquired by morphology repair operation, and it ensures the integrity of the canopy region and the independent noise removal. In order to verify the effect of the method proposed in this paper, 320 test images were captured from the cotton producing areas in Xinjiang, China from April to July 2016. The acquisition was often on sunny day, aiming at obtaining images under different lighting conditions, different positions in cotton field, and different cotton growth periods. These images were collected by the Canon EOS5D digital camera with 6 912×3 416 pixels, and zoomed into 1 728×1 152 pixels to improve segmentation effect. This algorithm programming development environment is Python 2.7, and OpenCV 2.4.9. The experimental results show that the average relative object area error (RAE) by our method is only 5.46%, the Exg feature OTSU method 11.78%, the four-component segmentation method 24.11%, and the saliency segmentation method 36.92%. The overall average matching rate by our method is 93.07%, the Exg feature OTSU method 76.43%, the four-component segmentation method 71.67%, and the saliency segmentation method 66.92%. The average processing time of this paper proposed method was 4.63 s, which was much more time-consuming than the super-green characteristic OTSU method (3.84 s) and the four-component segmentation method (2.56 s), but this time less than that of the segmentation method (6.25 s). Therefore the proposed method in our paper has better performance than other methods in cotton canopy segmentation task, and is effective to segment the cotton canopy in the complicated background and different cotton growth periods. The proposed method can provide certain basis for implementation of cotton growth condition automatic monitoring.