| Cite this article: | 张慧,伍萍辉,张馨,等.非垂直拍摄获取细叶作物覆盖度优化算法研究[J].灌溉排水学报,0,():-. |
| ZHANG Hui,WU Pinghui,ZHANG Xin,et al.非垂直拍摄获取细叶作物覆盖度优化算法研究[J].灌溉排水学报,0,():-. |
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| Research on Optimization Algorithm of Fine Leaf Crop Coverage Based on Non-Vertical Shooting |
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ZHANG Hui1, WU Pinghui1, ZHANG Xin2, SUN Tiejun3, XUE Xuzhang4, ZHENG Wengang4
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1.School of Electronic Information Engineering,Hebei University of Technology;2.Beijing Research Center for Information Technology in Agriculture;3.Beijing grass industry and environment research center,Beijing academy of agriculture and forestry;4.Beijing Research Center for Information Technology in Agriculture
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| Abstract: |
| 【Objective】To explore the optimization algorithm of fine leaf crop coverage and the effect of non-vertical shooting on crop coverage in non-vertical shooting.【Method】this paper takes Carex breviculmis R. Br. and Zoysia japonica Steud. as experimental objects in the open environment of the Xiaotangshan Base Grass Research Center in Beijing, and obtains images from three shooting angles of 45°, 60° and 90° (including the Angle with the ground). The image segmentation method based on adaptive weight particle swarm optimization K-means Was used to analyze the influence of different shooting angles on the measurement accuracy of Carex breviculmis R. Br. coverage, and the relationship between non-vertical shooting and coverage under vertical angle is studied. Firstly, the RGB image of grass is transformed into HSV color space. In the HSV color space, the adaptive weight PSO algorithm is used to search the subspace of the global pixel solution. The global optimal solution is found by iterative search to determine the best initial cluster center. Secondly, the k-means algorithm is used to cluster the pixels of different angles to obtain the result of segmentation of the canopy layer. Finally, the segmentation result is optimized by the morphological filtering method.【Results】The results show that the proposed method is superior to the traditional K-means method in both light adaptation and complex environment. It can overcome the problem of uneven color distribution caused by multi-angle shooting and effectively segment the canopy layer area. The relative error and root mean square error are all below 0.2, which is higher than the accuracy of the traditional K-means algorithm.【Conclusion】The calculated coverage under three angles is significantly linear. The research results can provide an effective way for machine vision to monitor crop coverage in a natural complex environment. |
| Key words: image segmentation; K-means algorithm; PSO optimization algorithm; multi-angle; coverage |
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