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


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.


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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.


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).

Author information




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