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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.

In order to realize efficient water-saving irrigation of strawberry greenhouse in precision agriculture, the paper proposed an automatic irrigation algorithm based on fuzzy comprehensive evaluation irrigation based on PLC and the configuration software, and build a remote control system. By measuring the greenhouse soil parameters and strawberry soil moisture data to determine the upper and lower limits of irrigation and set buried position of moisture sensors. Use fuzzy comprehensive evaluation method to handle multi-sensor data, and develop an automatic irrigation control algorithm that depends on the strawberry irrigation system. Set up a C / S mode of automatic irrigation control system through the 3G network and the remote computer. The experimental results show that the algorithm is reasonable and reliable, and can be well adapted to the strawberry greenhouse. The automatic irrigations meet the steady demands for real-time performance and high scalability. The irrigation automation of strawberry greenhouse provides a theoretical and practical basis for intelligent irrigation of strawberry greenhouse.

According to the problem of the low matching accuracy rate and the low speed in the matching technology for image search, we choose the image of the strawberry diseases as the object of the research, to extract the feature of strawberry diseases image and then do cluster analysis using SPSS, and choose the feature combination according to the clustering effect. Do search matching experiments on the feature combination by KD-Tree matching algorithm respectively, and determine the characteristics combination which search matching accuracy is higher by comparing the results. This feature combination of strawberry powdery mildew matching accuracy reached 83.3%, the deformity of strawberry matching accuracy reached 60.0%, the aim is to provide the research basis for rapid disease diagnosis on strawberry.