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Research Article

A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting

Sambuddha Ghosal1,2, Bangyou Zheng3, Scott C. Chapman3,4, Andries B. Potgieter5, David R. Jordan6, Xuemin Wang6, Asheesh K. Singh7, Arti Singh7, Masayuki Hirafuji8, Seishi Ninomiya8, Baskar Ganapathysubramanian1, Soumik Sarkar1, and Wei Guo8

1Department of Mechanical Engineering, Iowa State University, Ames, IA, USA
2Department of Computer Science, Iowa State University, Ames, IA, USA
3CSIRO Agriculture and Food, St. Lucia, QLD, Australia
4School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD 4343, Australia
5Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Gatton, QLD, Australia
6Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Warwick, QLD, Australia
7Department of Agronomy, Iowa State University, Ames, IA, USA
8International Field Phenomics Research Laboratory, Institute for Sustainable Agro-Ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
Correspondence should be addressed to Soumik Sarkar; soumiks@iastate.edu and Wei Guo; guowei@isas.a.u-tokyo.ac.jp

How to Cite this Article

Sambuddha Ghosal, Bangyou Zheng, Scott C. Chapman, et al., “A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting,” Plant Phenomics, vol. 2019, Article ID 1525874, 14 pages, 2019. https://doi.org/10.34133/2019/1525874.

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