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首页> 外文期刊>配管·装置·プラント技術 >Studies on precision planting system for plug seedling production using machine vision (part 1 ) - detection of seed blobs in tray-cells by a new grid method
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Studies on precision planting system for plug seedling production using machine vision (part 1 ) - detection of seed blobs in tray-cells by a new grid method

机译：基于机器视觉的插秧生产精密种植系统的研究（第1部分）-通过新的网格方法检测托盘细胞中的种子斑点

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A grid method to detect the existence of vegetable seeds into the tray-cells after the seeding operation was developed. Its features include generation of windows, noise suppression and grid operators. Windows are generated and processed at the cavities of the tray instead of processing the whole image of the tray. The image noises are suppressed by using local average filter kernel operation. Then the seed blobs are detected using grid operators. Judgement to whether the blob represented a seed or noise was carried out according to continuity of the grids that represent an object in the 8-or 16-neighborhood. To test the performance of the developed grid method, morphological features of the seeds and noises were extracted and analyzed. The results showed that the grid method suppressed the image noises by 76.9% and successfully detected the seed blobs. The detection accuracy was 100% for cucumber, melon, lettuce, eggplant and green pepper seeds, and was 99.2% for tomato seeds. Hence, the usefulness of the method to detect the existence or non-existence of seeds into the tray-cells was confirmed.
机译：开发了一种网格方法，可以在播种后检测出托盘中是否存在蔬菜种子。其功能包括生成窗口，噪声抑制和网格运算符。 Windows在托盘的腔中生成和处理，而不是处理托盘的整个图像。通过使用局部平均滤波器内核操作可以抑制图像噪声。然后使用网格运算符检测种子斑点。根据表示8个或16个邻域中对象的网格的连续性来判断blob是表示种子还是噪声。为了测试改进的网格方法的性能，提取并分析了种子的形态特征和噪声。结果表明，网格法将图像噪声抑制了76.9％，并成功检测出种子斑点。黄瓜，甜瓜，生菜，茄子和青椒种子的检测准确度为100％，番茄种子为99.2％。因此，证实了检测托盘细胞中种子存在与否的方法的有用性。

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