Hengbiao Zheng1,2, Weijie Tang2,3, Tao Yang1

  • 1National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, China.

概括

超光谱成像与深度学习相结合,增强了米粒蛋白质含量预测. 这种方法通过准确识别关键基因,如OsmtSSB1L,以提高高质量的米育种,从而改善遗传研究.