Seedling-lump integrated non-destructive monitoring for automatic transplanting with Intel RealSense depth camera
Seedling-lump integrated non-destructive monitoring for automatic transplanting with Intel RealSense depth camera
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DOI：
10.1016/j.aiia.2019.09.001
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发表时间：
2019-09-01
影响因子：
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通讯作者：
Lakhiar, Imran Ali
中科院分区：
文献类型：
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作者：
Syed, Tabinda Naz;Jizhan, Liu;Lakhiar, Imran Ali
植物生长参数的无损检测是自动化育苗移栽中的一个重要问题。最近，已经提出了几种基于图像的监测方法，并可能开发用于几种农业应用。提出并开发了一种基于RealSense的机器视觉系统，用于近距离苗块综合监测。该策略基于近景深度信息。在此基础上，采用点云聚类和适当的算法对三维苗木模型进行分割。此外，还开发了数据处理流水线，对4个不同品种的苗木形态参数进行了评价。实验用4种不同的幼苗品种（辣椒、番茄、黄瓜和莴苣）进行，并在不同的光条件（光和暗）下进行训练。此外，分析结果表明，由于近距离近红外检测，在明亮和黑暗环境中没有发现显著差异（p B 0.05）。然而，结果显示，RealSense和手动方法之间的茎直径关系对于R2 = 0.68的黄瓜、R2 = 0.54的番茄、R2 = 0.35的辣椒和R2 = 0.58的生菜幼苗是成立的。而RealSense与人工方法的苗高相关性分别高于辣椒、番茄、黄瓜和生菜的R2 = 0.99、0.99、0.99和0.99。根据实验结果，可以得出结论，RGB-D集成监测系统与目的的方法，可以实践苗圃苗木最有前途的，没有高的劳动力要求，在易用性方面。该系统具有良好的稳定性和相关性，可用于植物生长监测。另外，对移栽机器人的实时视觉伺服操作具有一定的实用价值。2019年，作者。Elsevier B. V.代表KeAi Communications Co.制作和主持，这是CC BY许可下的开放获取文章（http：//creativecommons.org/licenses/by/4.0/）。
Non-destructive plant growth parameters measurement is an important concern in automatic-seedling transplanting. Recently, several image-based monitoring approaches have been proposed and potentially developed for several agricultural applications. The presented study proposed and developed a RealSense-based machine vision system for the close-shot seedling-lump integrated monitoring. The strategy was based on the close-shot depth information. Further, the point cloud clustering and suitable algorithms were applied to obtain the segmentation of 3D seedling models. In addition, the data processing pipeline was developed to assess the different morphological parameter of 4 different seedling varieties. The experiments were carried out with 4 different seedling varieties (pepper, tomato, cucumber, and lettuce) and trained under different light conditions (light and dark). Moreover, analysis results showed that there was not significantly different (p b 0.05) found towards light and dark environments due to close-shot near-infrared detection. However, the results revealed that the stem diameter relationship between RealSense and the manual method was found for R2 = 0.68 cucumber, R2 = 0.54 tomato, R2 = 0.35 pepper, and R2 = 0.58 lettuce seedlings. Whereas, the seedling height relationship between RealSense and the manual method was found higher than R2 = 0.99, 0.99, 0.99, and 0.99 for pepper, tomato, cucumber, and lettuce, respectively. Based on the experiment results, it was concluded that the RGB-D integrated monitoring system with the purposed method could be practiced for nursery seedlings most promisingly without high labour requirements in terms of ease of use. The system revealed a good sturdiness and relevance for plant growth monitoring. Additionally, it has the perspective for future practical value to real-time vision servo operations for transplanting robots.& COPY; 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).