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Canopy coverage is an important agronomic indicator. Image method is widely used in this field as a convenient, fast and accurate ground measurement method. Image background segmentation is the most critical step to obtain canopy coverage. Some segmentation algorithms have been limited to largeleaf plants or crops with relatively sparse growth. Few studies were on fine leaf crops, or no more valuable rules based on segmentation results. Therefore, taking wheat as an example, an IFOA-K-means algorithm based on HSV space was proposed. The K-means algorithm split the image background as a theoretical basis for obtaining coverage changes. Then the wavelet denoising algorithm was used to denoise the luminance component separately. The main segmentation algorithm was improved by the adaptive step size fruit fly algorithm (IFOA). The Kmeans algorithm was used to perform background segmentation on wheat images, and the global optimality of the adaptive fruit fly algorithm and local optimal features of the K-means algorithm were integrated to optimize the segmentation effect. The segmentation effect was better than the Ostu method based on genetic algorithm. It was better to remove the obvious interference factors such as drip irrigation belt, compared with the traditional Kmeans algorithm, the segmentation results were superior to the traditional algorithms in terms of running time and peak signal-to-noise ratio. The accuracy of wheat coverage was over 90%, the fit to the crop coefficient was as high as 0.9531, and the estimation of wheat growth status was estimated.