A universal transfer-learning-based detection model for characterizing vascular bundles in Phyllostachys

被引:12
|
作者
Xu, Haocheng [1 ,2 ]
Zhang, Ying [1 ,2 ]
Wang, Jiajun [1 ,2 ]
Li, Jing [3 ,4 ]
Zhong, Tuhua [1 ,2 ]
Ma, Xinxin [1 ,2 ]
Wang, Hankun [1 ,2 ]
机构
[1] Int Ctr Bamboo & Rattan, Inst New Bamboo & Rattan Based Biomat, Beijing 100102, Peoples R China
[2] Beijing Bamboo & Rattan Sci & Technol, Key Lab Natl Forestry & Grassland Adm, Beijing 100102, Peoples R China
[3] Chinese Acad Forestry, Res Inst Wood Ind, Beijing 100091, Peoples R China
[4] Key Lab Natl Forestry Grassland Adm Wood Sci & Tec, Beijing 100091, Peoples R China
关键词
Bamboo; Vascular bundle; Cross section; Radial distribution; MECHANICAL-PROPERTIES; BAMBOO;
D O I
10.1016/j.indcrop.2022.114705
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
A comprehensive understanding of vascular bundles is the key to elucidate the excellent intrinsic mechanical properties of bamboo. This research aims to investigate the gradient distribution of fiber volume fraction and the gradient changes in the size of vascular bundles along the radial axis in Phyllostachys. The inter-nodes of twenty-nine kinds of Phyllostachys were collected, which the cross section was sanded by sanding pads with 320 mesh and scanned with a resolution of 9600 ppi. A universal transfer-learning-based vascular bundle detection model with high precision of up to 96.97% were built, which can help to obtain the characteristics of vascular bundles quickly and accurately. The total number of vascular bundles, total fiber sheath area, the length, width and area of fiber sheath of individual vascular bundles within the entire cross-section were counted and analyzed. The results showed that these parameters had a strongly positive linear correlation with the outer circumference and wall thickness of bamboo culms, but the fiber volume fraction (25.50 +/- 3.51%) and the length-to-width ratio of the vascular bundles (1.226 +/- 0.091) were relatively constant. Furthermore, the cross section of bamboo were divided into multi-layer sheet along the wall thickness direction and the characteristics of vascular bundle were counted in each layer. The results showed that the fiber volume fraction decreased exponentially along the radial direction from skin to core, the length-to-width ratio of vascular bundle decreased quadratically along the radial direction from skin to core, the width of vascular bundle increased linearly along the radial direction from skin to core. The trends of the gradient change in vascular bundle's characteristics were found highly consistent among bamboo species in Phyllostachys.
引用
收藏
页数:9
相关论文
共 50 条
  • [41] Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data
    Bloch, Victor
    Frondelius, Lilli
    Arcidiacono, Claudia
    Mancino, Massimo
    Pastell, Matti
    SENSORS, 2023, 23 (05)
  • [42] Transfer-learning-based approach for leaf chlorophyll content estimation of winter wheat from hyperspectral data
    Zhang, Yao
    Hui, Jian
    Qin, Qiming
    Sun, Yuanheng
    Zhang, Tianyuan
    Sun, Hong
    Li, Minzan
    REMOTE SENSING OF ENVIRONMENT, 2021, 267
  • [43] Artificial Hummingbird Algorithm with Transfer-Learning-Based Mitotic Nuclei Classification on Histopathologic Breast Cancer Images
    Malibari, Areej A. A.
    Obayya, Marwa
    Gaddah, Abdulbaset
    Mehanna, Amal S. S.
    Hamza, Manar Ahmed
    Alsaid, Mohamed Ibrahim
    Yaseen, Ishfaq
    Abdelmageed, Amgad Atta
    BIOENGINEERING-BASEL, 2023, 10 (01):
  • [44] Spectral transfer-learning-based metasurface design assisted by complex-valued deep neural network
    Xu, Yi
    Li, Fu
    Gu, Jianqiang
    Bi, Zhiwei
    Cao, Bing
    Yang, Quanlong
    Han, Jiaguang
    Hu, Qinghua
    Zhang, Weili
    ADVANCED PHOTONICS NEXUS, 2024, 3 (02):
  • [45] Deep Transfer Learning Based Rice Plant Disease Detection Model
    Narmadha, R. P.
    Sengottaiyan, N.
    Kavitha, R. J.
    INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2022, 31 (02): : 1257 - 1271
  • [46] A multi-label waste detection model based on transfer learning
    Zhang, Qiang
    Yang, Qifan
    Zhang, Xujuan
    Wei, Wei
    Bao, Qiang
    Su, Jinqi
    Liu, Xueyan
    RESOURCES CONSERVATION AND RECYCLING, 2022, 181
  • [47] A Reminiscent Intrusion Detection Model Based on Deep Autoencoders and Transfer Learning
    dos Santos, Roger R.
    Viegas, Eduardo K.
    Santin, Altair O.
    2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2021,
  • [48] Explainable Transfer Learning-Based Deep Learning Model for Pelvis Fracture Detection
    Kassem, Mohamed A. A.
    Naguib, Soaad M. M.
    Hamza, Hanaa M. M.
    Fouda, Mostafa M. M.
    Saleh, Mohamed K. K.
    Hosny, Khalid M. M.
    INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2023, 2023
  • [49] TumorDet: A Breast Tumor Detection Model Based on Transfer Learning and ShuffleNet
    Zhang, Tao
    Pan, Leying
    Yang, Qiang
    Yang, Guoping
    Han, Nan
    Qiao, Shaojie
    CURRENT BIOINFORMATICS, 2024, 19 (02) : 119 - 128
  • [50] Transfer learning based SSD model for helmet and multiple rider detection
    Nandhini C.
    Brindha M.
    International Journal of Information Technology, 2023, 15 (2) : 565 - 576