Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model.

Zhang, Jiajing; Min, An; Steffenson, Brian J; Su, Wen-Hao; Hirsch, Cory D; Anderson, James; Wei, Jian; Ma, Qin; Yang, Ce

Zhang, Jiajing; Min, An; Steffenson, Brian J; Su, Wen-Hao; Hirsch, Cory D; Anderson, James; Wei, Jian; Ma, Qin; Yang, Ce.

Afiliación

Zhang J; College of Information and Electrical Engineering, China Agricultural University, Beijing, China.
Min A; The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Steffenson BJ; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
Su WH; Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN, United States.
Hirsch CD; Department of Plant Pathology, University of Minnesota, Saint Paul, MN, United States.
Anderson J; College of Engineering, China Agricultural University, Beijing, China.
Wei J; Department of Plant Pathology, University of Minnesota, Saint Paul, MN, United States.
Ma Q; Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, MN, United States.
Yang C; College of Information and Electrical Engineering, China Agricultural University, Beijing, China.

Front Plant Sci ; 13: 834938, 2022.

Article en En | MEDLINE | ID: mdl-35222491

Palabras clave

Hybrid Task Cascade model; challenging dataset; instance segmentation; non-structural field; wheat spike

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Plant Sci Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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