AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Plant Phenomics Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

TrichomeYOLO: A Neural Network for Automatic Maize Trichome Counting

Jie Xu1,2,†, Jia Yao3,†, Hang Zhai1,2, Qimeng Li1,2, Qi Xu1,2, Ying Xiang3, Yaxi Liu4, Tianhong Liu1,2, Huili Ma1,2, Yan Mao5, Fengkai Wu1,2, Qingjun Wang1,2, Xuanjun Feng1,2, Jiong Mu3( ), Yanli Lu1,2( )
Maize Research Institute, Sichuan Agricultural University, Wenjiang 611130, Sichuan, China
State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Wenjiang 611130, Sichuan, China
College of Information Engineering, Sichuan Agricultural University, Yaan 625014, Sichuan, China
Triticeae Research Institute, Sichuan Agricultural University, Wenjiang 611130, Sichuan, China
College of Chemistry and Life Sciences, Chengdu Normal University, Wenjiang 611130, Sichuan, China

†These authors contributed equally to this work.

Show Author Information

Abstract

Plant trichomes are epidermal structures with a wide variety of functions in plant development and stress responses. Although the functional importance of trichomes has been realized, the tedious and time-consuming manual phenotyping process greatly limits the research progress of trichome gene cloning. Currently, there are no fully automated methods for identifying maize trichomes. We introduce TrichomeYOLO, an automated trichome counting and measuring method that uses a deep convolutional neural network, to identify the density and length of maize trichomes from scanning electron microscopy images. Our network achieved 92.1% identification accuracy on scanning electron microscopy micrographs of maize leaves, which is much better performed than the other 5 currently mainstream object detection models, Faster R-CNN, YOLOv3, YOLOv5, DETR, and Cascade R-CNN. We applied TrichomeYOLO to investigate trichome variations in a natural population of maize and achieved robust trichome identification. Our method and the pretrained model are open access in Github (https://github.com/yaober/trichomecounter). We believe TrichomeYOLO will help make efficient trichome identification and help facilitate researches on maize trichomes.

References

1

Handley R, Ekbom B, Ågren J. Variation in trichome density and resistance against a specialist insect herbivore in natural populations of Arabidopsis thaliana. Ecol Entomol. 2005;30(3):284–292.
Crossref Google Scholar
2

Bonierbale MW, Plaisted RL, Pineda O, Tanksley SD. QTL analysis of trichome-mediated insect resistance in potato. Theor Appl Genet. 1994;87(8):973–987.
Crossref Google Scholar
3

Johnson CS, Kolevski B, Smyth DR. TRANSPARENT TESTA GLABRA2, a trichome and seed coat development gene of Arabidopsis, encodes a WRKY transcription factor. Plant Cell. 2002;14(6):1359–1375.
Crossref Google Scholar
4

Gasparini K, da Silva MF, Costa LC, Martins SCV, Ribeiro DM, Peres LEP, Zsögön A. The Lanata trichome mutation increases stomatal conductance and reduces leaf temperature in tomato. J Plant Physiol. 2021;260:Article 153413.
Crossref Google Scholar
5

Fambrini M, Pugliesi C. The dynamic genetic-hormonal regulatory network controlling the Trichome development in leaves. Plants (Basel). 2019;8(8):Article 253.
Crossref Google Scholar
6

Hauser M-T. Molecular basis of natural variation and environmental control of trichome patterning. Front Plant Sci. 2014;5:320.
Crossref Google Scholar
7

Walker AR, Davison PA, Bolognesi-Winfield AC, James CM, Srinivasan N, Blundell TL, Esch JJ, Marks MD, Gray JC. The TRANSPARENT TESTA GLABRA1 locus, which regulates trichome differentiation and anthocyanin biosynthesis in Arabidopsis, encodes a WD40 repeat protein. Plant Cell. 1999;11(7):1337–1349.
Crossref Google Scholar
8

Payne CT, Zhang F, Lloyd AM. GL3 encodes a bHLH protein that regulates trichome development in Arabidopsis through interaction with GL1 and TTG1. Genetics. 2000;156(3):1349–1362.
Crossref Google Scholar
9

Zhang F, Gonzalez A, Zhao M, Payne CT, Lloyd A. A network of redundant bHLH proteins functions in all TTG1-dependent pathways of Arabidopsis. Development. 2003;130(20):4859–4869.
Crossref Google Scholar
10

Nesi N, Debeaujon I, Jond C, Pelletier G, Caboche M, Lepiniec L. The TT8 gene encodes a basic helix-loop-helix domain protein required for expression of DFR and BAN genes in Arabidopsis siliques. Plant Cell. 2000;12(10):1863–1878.
Crossref Google Scholar
11

Mochida K, Koda S, Inoue K, Hirayama T, Tanaka S, Nishii R, Melgani F. Computer vision-based phenotyping for improvement of plant productivity: A machine learning perspective. Gigascience. 2019;8(1):Article giy153.
Crossref Google Scholar
12

Jiang Y, Li C. Convolutional neural networks for image-based high-throughput plant phenotyping: A review. Plant Phenomics. 2020;2020:Article 4152816.
Crossref Google Scholar
13

Zhang C, Zhou L, Xiao Q, Bai X, Wu B, Wu N, Zhao Y, Wang J, Feng L. End-to-end fusion of hyperspectral and chlorophyll fluorescence imaging to identify rice stresses. Plant Phenomics. 2022;2022:Article 9851096.
Crossref Google Scholar
14

Borhani Y, Khoramdel J, Najafi E. A deep learning based approach for automated plant disease classification using vision transformer. Sci Rep. 2022;12(1):11554.
Crossref Google Scholar
15

Johnson J, Sharma G, Srinivasan S, Masakapalli SK, Sharma S, Sharma J, Dua VK. Enhanced field-based detection of potato blight in complex backgrounds using deep learning. Plant Phenomics. 2021;2021:Article 9835724.
Crossref Google Scholar
16

Ferentinos KP. Deep learning models for plant disease detection and diagnosis. Comput Electron Agric. 2018;145:311–318.
Crossref Google Scholar
17

Xiao Q, Tang W, Zhang C, Zhou L, Feng L, Shen J, Yan T, Gao P, He Y, Wu N. Spectral preprocessing combined with deep transfer learning to evaluate chlorophyll content in cotton leaves. Plant Phenomics. 2022;2022:Article 9813841.
Crossref Google Scholar
18

Teramoto S, Uga Y. A deep learning-based phenotypic analysis of Rice root distribution from field images. Plant Phenomics. 2020;2020:Article 3194308.
Crossref Google Scholar
19

Lu H, Cao Z, Xiao Y, Zhuang B, Shen C. TasselNet: Counting maize tassels in the wild via local counts regression network. Plant Methods. 2017;13:Article 79.
Crossref Google Scholar
20

DeChant C, Wiesner-Hanks T, Chen S, Stewart EL, Yosinski J, Gore MA, Nelson RJ, Lipson H. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology. 2017;107(11):1426–1432.
Crossref Google Scholar
21
Graves A, Mohamed A-r, Hinton G. Speech recognition with deep recurrent neural networks. Paper presented at: ICASSP 2013. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing; 2013 May 26–31; Vancouver, BC.
Crossref
22

Gustavsen B, Portillo Á. A damping factor-based white-box transformer model for network studies. IEEE Trans Power Deliv. 2018;33(6):2956–2964.
Crossref Google Scholar
23
Ren S, He K, Girshick R, Sun J. Faster r-cnn: Towards real-time object detection with region proposal networks. Paper presented at: NIPS 2015. Proceedings of the 28th International Conference on Neural Information Processing Systems; 2015 Dec 7–12; Montreal, Canada.
24

Singh S, Ahuja U, Kumar M, Kumar K, Sachdeva M. Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment. Multimed Tools Appl. 2021;80(13):19753–19768.
Crossref Google Scholar
25
Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. Paper presented at: CVPR 2016. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 27–30; Las Vegas, NV.
Crossref
26

Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell. 2017;40(4):834–848.
Crossref Google Scholar
27

Hasan MM, Chopin JP, Laga H, Miklavcic SJ. Detection and analysis of wheat spikes using convolutional neural networks. Plant Methods. 2018;14(1):Article 100.
Crossref Google Scholar
28

Fetter KC, Eberhardt S, Barclay RS, Wing S, Keller SR. StomataCounter: A neural network for automatic stomata identification and counting. New Phytol. 2019;223(3):1671–1681.
Crossref Google Scholar
29

Liang X, Xu X, Wang Z, He L, Zhang K, Liang B, Ye J, Shi J, Wu X, Dai M, et al. StomataScorer: A portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model. Plant Biotechnol J. 2022;20(3):577–591.
Crossref Google Scholar
30
Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S. End-to-end object detection with transformers. Paper presented at: ECCV 2020. Proceedings of the 16th European Conference on Computer Vision; 2020 Aug 23–28; Glasgow, UK.
Crossref
31
Cai Z, Vasconcelos N. Cascade r-CNN: Delving into high quality object detection. Paper presented at: CVPR 2018. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition; 2018 Jun 18–23; Salt Lake City, UT.
Crossref
32

Ishida T, Kurata T, Okada K, Wada T. A genetic regulatory network in the development of trichomes and root hairs. Annu Rev Plant Biol. 2008;59:365–386.
Crossref Google Scholar
33
Park N, Kim S. How do vision transformers work? arXiv. 2022. https://arxiv.org/abs/2202.06709.
34
Tan M, Pang R, Le QV. Efficientdet: Scalable and efficient object detection. Paper presented at: CVPR 2020. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2020 Jun 13–19; Seattle, WA.
Crossref
35

Garcia A, Talavera-Mateo L, Santamaria ME. An automatic method to quantify trichomes in Arabidopsis thaliana. Plant Sci. 2022;323:Article 111391.
Crossref Google Scholar
36

Mirnezami SV, Young T, Assefa T, Prichard S, Nagasubramanian K, Sandhu K, Sarkar S, Sundararajan S, O'Neal ME, Ganapathysubramanian B, et al. Automated trichome counting in soybean using advanced image-processing techniques. Appl Plant Sci. 2020;8(7):Article e11375.
Crossref Google Scholar
37

Moose SP, Lauter N, Carlson SR. The maize macrohairless1 locus specifically promotes leaf blade macrohair initiation and responds to factors regulating leaf identity. Genetics. 2004;166(3):1451–1461.
Crossref Google Scholar
Plant Phenomics
Volume 5,
2023
Article number: 0024
DOI: 10.34133/plantphenomics.0024
Cite this article:
Xu J, Yao J, Zhai H, et al. TrichomeYOLO: A Neural Network for Automatic Maize Trichome Counting. Plant Phenomics, 2023, 5: 0024. https://doi.org/10.34133/plantphenomics.0024

138

Views

9

Crossref

6

Web of Science

8

Scopus

0

CSCD

Altmetrics
Received: 04 October 2022
Accepted: 17 January 2023
Published: 28 February 2023
© 2023 Jie Xu et al. Exclusive Licensee Nanjing Agricultural University. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License (CC BY 4.0).
Return