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A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice
Lingfeng Duan1,2, Wanneng Yang1,2, Chenglong Huang1,2
1Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, 1037 Luoyu Rd., Wuhan 430074, P.R. China.
Abstract:
The evaluation of yield-related traits is an essential step in rice breeding, genetic research and functional genomics research. A new, automatic, and labor-free facility to automatically thresh rice panicles, evaluate rice yield traits, and subsequently pack filled spikelets is presented in this paper. Tests showed that the facility was capable of evaluating yield-related traits with a mean absolute percentage error of less than 5% and an efficiency of 1440 plants per continuous 24 h workday.
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