Auto-LIA:基于视觉的叶倾角自动测量系统改善了植物生理监测。

IF 7.6 1区 农林科学 Q1 AGRONOMY
Sijun Jiang,Xingcai Wu,Qi Wang,Zhixun Pei,Yuxiang Wang,Jian Jin,Ying Guo,RunJiang Song,Liansheng Zang,Yong-Jin Liu,Gefei Hao
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引用次数: 0

摘要

植物传感器通常用于农业生产、园林绿化和其他领域,以监测植物生长和环境参数。作为植物监测的一个重要基本参数,叶倾角(LIA)不仅影响光吸收和农药流失,还有助于遗传分析和其他植物表型数据的收集。叶倾角的测量为作物研究和农业管理(如水分损失、农药吸收和光照辐射)提供了依据。一方面,以光探测和测距(LiDAR)为代表的现有高效解决方案可以提供地块的平均叶角分布。另一方面,以人工测量为代表的劳动密集型方案可以显示出较高的精确度。然而,现有方法存在自动化程度低和植物叶片相关性弱的问题,限制了对单个植物叶片表型的应用。为了提高叶片表型测量的效率,并提供叶片与植物之间的相关性,我们设计了一种基于图像表型的无创高效光学传感器测量系统,该系统结合了通过计算机视觉技术实现的多重处理和物理传感设备采集的 RGB 图像。具体来说,我们利用物体检测将叶子与植物联系起来,并采用三维重建技术在计算空间中恢复叶子的空间信息。然后,我们提出了一种基于空间连续性的分割算法,并结合图形操作来实现叶片关键点的提取。最后,我们寻求计算空间与实际物理空间之间的联系,提出了一种叶片变换的方法,以实现 LIA 在物理空间的定位和恢复。总之,我们的解决方案具有非侵入性、全过程自动化和植物叶片关联性强等特点,能以低成本实现高效测量。在这项研究中,我们验证了 Auto-LIA 的实用性,并将其准确性与使用昂贵的侵入式激光雷达设备获取的最佳解决方案进行了比较。我们的解决方案以更低的设备成本证明了其竞争力和可用性,精度仅比广泛使用的激光雷达低 2.5°。作为植物传感器信号的智能处理系统,Auto-LIA 可提供全自动的激光雷达测量,改善植物生理信息的监测,促进植物保护。我们在 http://autolia.samlab.cn 网站上公开了我们的代码和数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology.
Plant sensors are commonly used in agricultural production, landscaping, and other fields to monitor plant growth and environmental parameters. As an important basic parameter in plant monitoring, leaf inclination angle (LIA) not only influences light absorption and pesticide loss but also contributes to genetic analysis and other plant phenotypic data collection. The measurements of LIA provide a basis for crop research as well as agricultural management, such as water loss, pesticide absorption, and illumination radiation. On the one hand, existing efficient solutions, represented by light detection and ranging (LiDAR), can provide the average leaf angle distribution of a plot. On the other hand, the labor-intensive schemes represented by hand measurements can show high accuracy. However, the existing methods suffer from low automation and weak leaf-plant correlation, limiting the application of individual plant leaf phenotypes. To improve the efficiency of LIA measurement and provide the correlation between leaf and plant, we design an image-phenotype-based noninvasive and efficient optical sensor measurement system, which combines multi-processes implemented via computer vision technologies and RGB images collected by physical sensing devices. Specifically, we utilize object detection to associate leaves with plants and adopt 3-dimensional reconstruction techniques to recover the spatial information of leaves in computational space. Then, we propose a spatial continuity-based segmentation algorithm combined with a graphical operation to implement the extraction of leaf key points. Finally, we seek the connection between the computational space and the actual physical space and put forward a method of leaf transformation to realize the localization and recovery of the LIA in physical space. Overall, our solution is characterized by noninvasiveness, full-process automation, and strong leaf-plant correlation, which enables efficient measurements at low cost. In this study, we validate Auto-LIA for practicality and compare the accuracy with the best solution that is acquired with an expensive and invasive LiDAR device. Our solution demonstrates its competitiveness and usability at a much lower equipment cost, with an accuracy of only 2. 5° less than that of the widely used LiDAR. As an intelligent processing system for plant sensor signals, Auto-LIA provides fully automated measurement of LIA, improving the monitoring of plant physiological information for plant protection. We make our code and data publicly available at http://autolia.samlab.cn.
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来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
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