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In this paper, we propose a segmenting method for leaf images taken in the field. Instead of processing images in the whole grayscale range, we choose several subintervals of grayscale to operate on. The subintervals are chosen in accordance with the envelopes of image histograms. The subinterval is composed of random two local minimum points. In order to examine the effect of the method, four evaluation grades are settled as ‘Perfect (P)’, ‘Well (W)’, ‘Average (A)’ and ‘Unsatisfying (U)’. ‘Perfect’ (P) represents the leaf edge segmented is complete; ‘Well’ (W) represents leaf edge segmented is almost complete; ‘Average’ (A) represents there is no complete leaf edge segmented and ‘Unsatisfied’ (U) indicates that the majority of the leaf edge is not segmented from the background. Operating the segmentation on different subintervals and clustering the segmenting results into these four grades, we could obtain the suitable grayscale subintervals; therefore the subintervals chosen before have been optimized. Experiments on four kinds of leaves including jujube, strawberry, begonia and jasmine leaves shows that optimized grayscale interval solutions are sufficient to segment target leaf from the background with high efficiency and accuracy.

Image segmentation plays an important role in the field of digital production management. Image resolution is an important factor affecting the size of its segmentation and segmentation efficiency, and the physical characteristics of the image capturing device is another important factor. With high-resolution segmentation algorithm in image segmentation, we often find that the edge contour image segmentation is difficult to accurately determine, more complex image arithmetic operation efficiency is not high and images taken with a different device in response to segmentation algorithms are very different. In this paper, the plant leaf image collected from different cameras was used as the object of study, and the feature quantity was extracted. The appropriate segmentation boundary was determined by cluster analysis. The leaf image was pretreated with the resolution adjustment, and the leaf image was in the appropriate segmentation feature range. After the clustering domain processing of the feature range in this paper, it solves the problem that the real edge of the leaf area information is too difficult to distinguish, and effectively solves the problem of complex image algorithm and ordinary pc machine in the process of complex image processing Efficiency issues. The appropriate segmentation feature range of the devices is established for different devices, which effectively solves the different response of different devices to the segmentation algorithm.