Abstract: To solve the problems that convolution neural network has some disadvantages in bunch of bananas recognition, including large scale training samples, long training time, high hardware requirements, and that traditional image processing methods are easily affected by illumination and background color, which results in recognizing an incomplete bunch of bananas with many noise points, a bunch of bananas recognition method based on image background saturation compression, different threshold segmentation, and fusion is proposed. Firstly, the high and low saturation thresholds of bunch of bananas potential regions are adaptively determined according to the pixel proportion of the grayscale in the saturation image. Then, gamma transform is performed on the image background saturation, which is less than the low saturation threshold, and half value compression is performed on the image background, which is greater than the high saturation threshold, to enhance the saturation contrast between bunch of bananas potential region and environment background. Then, the difference image of saturation component and hue component is segmented by using large and small threshold range, respectively. Environmental background noise is extracted from the threshold segmentation result by hole filling and connected domain extracting. The segmentation result of large and small threshold range is the difference fused for reducing background noise points to get the bunch of bananas with higher accuracy and fewer noise points. The experimental results show that the accuracy of bunch of bananas recognition higher than 0.85 is accounted for 39.29%, between 0.80 and 0.85 for 46.43%, and less than 0.80 for 14.28% with the images sampled in a natural banana plantation environment. This method can adapt to the bunch of bananas recognition under different light and environmental colors.