利用径向生长修复算法检测玉米根系表型. (Chinese)

This study aims to improve the detection precision of plant root phenotyping in the images, particularly with broken roots. An algorithm of radial growing repair was proposed to apply to the evaluation of maize seed’s resistance to damage. A soil culture experiment was also conducted on the Pukou campus of Nanjing Agricultural University in China every month. After that, four varieties of corn seeds were selected: Yufeng 303 (ordinary control group), Zhengdan 958 (drought resistance group), Liyu 16 (water resistance group), and Zhengdan 958 (salt resistance group). Four kinds of resistant corn seeds were planted in trough-shaped and flat containers for 14d, including ordinary, drought-resistant, water-resistant, and salt-resistant corn seeds. Subsequently, the root system was taken out to rinse the residual soil with tap water, and then placed on a solid-color background plate to level out. Prior to image acquisition, the root length and diameter were measured by a ruler, and the number of main and lateral roots was counted to record. The specific procedure of image processing was as follows. Firstly, a series of operations was used to preprocess the collected images for the extraction of root systems from complex backgrounds, such as adaptive contrast enhancement, histogram grayscale searching, and pepper-salt denoising. As such, the discrimination of root images was improved to remove the noise during image acquisition, such as reflections and water stains. Secondly, the tips of main roots in maize images were detected by training the YOLO-V3 neural network. Finally, the radial growth repair algorithm was presented, including the direction discrimination of main roots in bifurcation points, and adaptive repair in end points. These strategies greatly contributed to extracting phenotypic parameters from main and lateral roots. Maize root datasets were also selected to evaluate the practicality and accuracy of radial growth repair. The results demonstrated that the phenotypic accuracy of repaired main roots lengths and diameter increased from from 83.6% and 84.4% to 97.4% and 94.8%, respectively, compared with that processed by region growth algorithm. The phenotypic parameters extracted by radial growth repair algorithm was more precise than that extracted by region growth algorithm, which indicated that radial growth repair algorithm was suitable for extraction of maize root system parameters in different stress environments. The accuracy of radial growth was improved more obviously in the salty environments and drought environments. The results in this study demonstrated that the proposed radial growth repair algorithm could improve the accuracy of root image phenotype detection and could be efficient for maize resistance evaluation, which provided reference for the rapid extraction of root system phenotype. [ABSTRACT FROM AUTHOR]

针对根系图像中的断根易导致根系表型信息难以精确获取的问题，该研究提出一种根系径向生长修复算法， 并基于此进行不同抗性玉米种子根系表型对比研究。首先，采用自适应对比度增强、直方图灰度查找、椒盐去噪等对 采集的根系图像进行预处理，从复杂背景中分离出根系图像；再通过 YOLO-V3 检测模型进行根系图像中主根根尖识 别；最后，自根尖开始进行径向生长，通过分叉点主根提取策略、端点自适应修复策略实现主根图像修复，并提取主 根和侧根表型信息。将普通、抗旱、抗涝、抗盐 4 种不同抗性的玉米种子种植于槽型扁平容器中培养 14 d 后取出，冲 洗得到完整根系并进行图像采集。采用径向生长修复算法进行根系修复后提取根系长度和直径与根系图像修复前相比， 根系长度和直径的提取精度分别由 83.6%和 84.4%提高至 97.4%和 94.8%，径向生长修复算法提取精度优于区域生长算 法，适用于不同胁迫环境下玉米根系表型参数提取。在干旱环境和盐腌环境下，径向生长修复算法精度提升更明显。 结果表明，本研究所提算法所提出的根系径向生长修复算法可有效提高根系图像表型信息精度，为根系表型快速提取 提供参考。 [ABSTRACT FROM AUTHOR]

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