A Novel Approach to Pod Count Estimation Using a Depth Camera in Support of Soybean Breeding Applications.

Mathew, Jithin; Delavarpour, Nadia; Miranda, Carrie; Stenger, John; Zhang, Zhao; Aduteye, Justice; Flores, Paulo

Mathew, Jithin; Delavarpour, Nadia; Miranda, Carrie; Stenger, John; Zhang, Zhao; Aduteye, Justice; Flores, Paulo.

Afiliación

Mathew J; Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USA.
Delavarpour N; Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USA.
Miranda C; Department of Plant Sciences, North Dakota State University, Fargo, ND 58105, USA.
Stenger J; North Dakota Agricultural Weather Network, School of Natural Resource Sciences, North Dakota State University, Fargo, ND 58105, USA.
Zhang Z; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
Aduteye J; Department of Agronomy, Earth University, San Jose 4442-1000, Costa Rica.
Flores P; Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND 58105, USA.

Sensors (Basel) ; 23(14)2023 Jul 18.

Article en En | MEDLINE | ID: mdl-37514799

Asunto(s)

Glycine max; Fitomejoramiento

Palabras clave

background segmentation; computer vision; deep learning; depth camera; high throughput phenotyping; machine vision; soybean pod-counting

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Glycine max / Fitomejoramiento Tipo de estudio: Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google