A high-throughput and low-cost maize ear traits scorer

Abstract

In this study, based on automatic control and image processing, a high-throughput and low-cost maize ear traits scorer (METS) was developed for the automatic measurement of 34 maize ear traits. In total, 813 maize ears were measured using METS, and the results showed that the square of the correlation coefficient (R2) of the manual measurements versus the automatic measurements for ear length, ear diameter, and kernel thickness were 0.96, 0.79, and 0.85, respectively. These maize ear traits could be used to classify the type, and the results showed that the classification accuracy of the support vector machine (SVM) model for the test set was better than that of the random forest (RF) model. In addition, the general applicability of the image analysis pipeline was also demonstrated on other independent maize ear phenotyping platforms. In conclusion, equipped with image processing and automatic control technologies, we have developed a high-throughput method for maize ear scoring, which could be popularized in maize functional genetics, genomics, and breeding applications.

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Data availability

The source code and data for this study can be accessed via http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action.

References

  1. Byun H, Lee SW (2002) Applications of support vector machines for pattern recognition: a survey. Lect Notes Comput Sci 2388:213–236

    Article  Google Scholar 

  2. Chen Y, Xiao C, Chen X, Li Q, Zhang J, Chen F, Yuan L, Mi G (2014) Characterization of the plant traits contributed to high grain yield and high grain nitrogen concentration in maize. Field Crop Res 159:1–9. https://doi.org/10.1016/j.fcr.2014.01.002

    Article  Google Scholar 

  3. Duan L, Yang W, Huang C, Liu Q (2011) A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice. Plant Methods 7:44. https://doi.org/10.1186/1746-4811-7-44

    Article  PubMed  PubMed Central  Google Scholar 

  4. Dubey BP, Bhagwat SG, Shouche SP, Sainis JK (2006) Potential of artificial neural networks in varietal identification using morphometry of wheat grains. Bios Engin 95:61–67. https://doi.org/10.1016/j.biosystemseng.2006.06.001

    Article  Google Scholar 

  5. Emerson RW (2015) Convenience sampling, random sampling, and snowball sampling: how does sampling affect the validity of research? J Visual Impair Blind 109(2):164

    Article  Google Scholar 

  6. Hu W, Zhang C, Jiang Y, Huang C, Liu Q, Xiong L, Yang W, Chen F (2020) Nondestructive 3D image analysis pipeline to extract rice grain traits using X-ray computed tomography. Plant Phenom 12:3414926. https://doi.org/10.34133/2020/3414926

    Article  Google Scholar 

  7. Huang H, Zhang DJ, Zhan SY, Shen Y, Wang HZ, Song H, Xu J, He Y (2019) Research on sample division and modeling method of spectrum detection of moisture content in dehydrated scallops. Spectrosc Spectr Anal 39(1):185–192. (in Chinese with English abstract). https://doi.org/10.3964/j.issn.1000-0593(2019)01-0185-08

    CAS  Article  Google Scholar 

  8. Igathinathane C, Pordesimo LO, Batchelor WD (2009) Major orthogonal dimensions measurement offood grains by machine vision using ImageJ. Food Res Int 42:76–84. https://doi.org/10.1016/j.foodres.2008.08.013

    Article  Google Scholar 

  9. Li L, Zhang Q, Huang D (2014) A review of imaging techniques for plant phenotyping. Sensors 14:20078–20111. https://doi.org/10.3390/s141120078

    Article  PubMed  Google Scholar 

  10. Liang X, Wang K, Huang C, Zhang X, Yan J, Yang W (2016) A high-throughput maize kernel traits scorer based on line-scan imaging. Measurement 90:453–460. https://doi.org/10.1016/j.measurement.2016.05.015

    Article  Google Scholar 

  11. Liu DY, Zhang W, Liu YM, Chen XP, Zou CQ (2020) Soil application of zinc fertilizer increases maize yield by enhancing the kernel number and kernel weight of inferior grains. Front Plant Sci 11:188. https://doi.org/10.3389/fpls.2020.00188

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ma Q, Jiang J, Zhu D, Li S, Mei S (2012) Rapid measurement for 3D geometric features of maize ear based on image processing. Transac Chin Soc Agricult Eng 28(supp.2):208–212. https://doi.org/10.3969/j.issn.1002-6819.2012.z2.036

    CAS  Article  Google Scholar 

  13. Mebatsion HK, Paliwal J, Jayas DS (2013) Automatic classification of non-touching cereal grains in digital images using limited morphological and color features. Comput Electron Agric 90(1):99–105. https://doi.org/10.1016/j.compag.2012.09.007

    Article  Google Scholar 

  14. Panigrahi S, Misra MK, Willson S (1998) Evaluations of fractal geometry and invariant moments for shape classification of corn germplasm. Comput Electron Agric 20(1):1–20. https://doi.org/10.1016/S0168-1699(98)00004-0

    Article  Google Scholar 

  15. Rahman MA, Hossain MF, Hossain M, Ahmmed R (2020) Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal. Egypt Inform J 21:23–35. https://doi.org/10.1016/j.eij.2019.10.002

    Article  Google Scholar 

  16. Tanabata T, Shibaya T, Hori K, Ebana K, Yano M (2012) SmartGrain: high-throughput phenotyping software for measuring seed shape through image analysis. Plant Physiol 160:1871–1880. https://doi.org/10.1104/pp.112.205120

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Venter G, Sobieszczanski-Sobieski J (2003) Particle swarm optimization. AIAA J 41(8):1583–1589

    Article  Google Scholar 

  18. Wang H, Sun Y, Zhang T, Zhang G, Li Y, Liu T (2010) Appearance quality grading for fresh corn ear using computer vision. Transac Chin Soc Agricult Machin 41(8):156–159,165. (in Chinese with English abstract). https://doi.org/10.3969/j.issn.1000-1298.2010.08.032

    CAS  Article  Google Scholar 

  19. Wang C, Guo X, Wu S, Du J (2013) Investigate maize ear traits using machine vision with panoramic photograyphy. Transac Chin Soc Agricult Eng 29(24):155–162. (in Chinese with English abstract). https://doi.org/10.3969/j.issn.1002-6819.2013.24.021

    CAS  Article  Google Scholar 

  20. Wu G, Miller ND, Leon N, Kaeppler SM, Spalding EP (2020) Predicting Zea mays flowering time, yield, and kernel dimensions by analyzing aerial images. Front Plant Sci 10:1251. https://doi.org/10.3389/fpls.2019.01251

    Article  Google Scholar 

  21. Yang J, Zhang H, Zhao Y, Song X, Wang X (2010) Quantitative study on the relationships between grain yield and ear 3-D geometry in maize. Sci Agric Sin 43(21):4367–4374. (in Chinese with English abstract). https://doi.org/10.1097/MOP.0b013e3283423f35

    CAS  Article  Google Scholar 

  22. Zhao C, Han Z, Yang J, Li N, Liang G (2009) Study on application of image process in ear traits for DUS testing in maize. Sci Agric Sin 42(11):4100–4105. (in Chinese with English abstract). https://doi.org/10.3864/j.issn.0578-1752.2009.11.043

    Article  Google Scholar 

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Materials availability

The source code and data for this study can be accessed via http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action.

Funding

This work was supported by the grant from the National Key Research and Development Program (2020YFD1000904-1-3), the National Natural Science Foundation of China (31770397), and the Fundamental Research Funds for the Central Universities (2662020GXPY010, 2662020ZKPY017).

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Contributions

Liang X. processed the images, analyzed the data, and wrote the paper. Ye J. designed the hardware of the system and performed parts of the experiments. Li X. conceived the experiment. Tang Z. analyzed parts of the data. Zhang X., Li W., and Yan J. provided the materials. Yang W. supervised this project and revised the paper.

Corresponding author

Correspondence to Wanneng Yang.

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The authors declare no competing interest.

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Liang, X., Ye, J., Li, X. et al. A high-throughput and low-cost maize ear traits scorer. Mol Breeding 41, 17 (2021). https://doi.org/10.1007/s11032-021-01205-4

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Keywords

  • Maize ear traits
  • High-throughput method
  • Image processing
  • Support vector machine (SVM)
  • Automatic measurement