Data/hora: 16/06/2024 09:25:07

Biblioteca(s): 	Embrapa Gado de Corte.	
Data corrente: 	17/10/2023	
Data da última atualização: 	17/10/2023	
Autoria: 	RODRIGUES, L. de S.; CAIXETA FILHO, E.; SAKIYAMA, K.; SANTOS, M. F.; JANK, L.; CARROMEU, C.; SILVEIRA, E.; MATSUBARA, E. T.; MARCATO JUNIOR, J.; GONCALVES, W. N.	
Afiliação: 	LUCAS DE SOUZA RODRIGUES, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; EDMAR CAIXETA FILHO, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; KENZO SAKIYAMA, UNIVERSIDADE DE SÃO PAULO; MATEUS FIGUEIREDO SANTOS, CNPGC; LIANA JANK, CNPGC; CAMILO CARROMEU, GTI; ELOISE SILVEIRA, CNPGC; EDSON TAKASHI MATSUBARA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; JOSÉ MARCATO JUNIOR, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; WESLEY NUNES GONCALVES, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL.	
Título: 	Deep4Fusion: a Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits.	
Ano de publicação: 	2023	
Fonte/Imprenta: 	Computers and Electronics in Agriculture, v. 211, 2023.	
Páginas: 	14 p.	
DOI: 	https://doi.org/10.1016/j.compag.2023.107957	
Idioma: 	Inglês	
Conteúdo: 	Deep learning methods have become one of the fundamental blocks of high-throughput phenotyping using RGB imagery. In this study, we go beyond applying deep learning algorithms; we improve deep learning models using a multi-view fusion approach. The proposal dynamically merges information from two deep-learning models. We evaluate this approach to improve the estimation of total dry matter yield, leaf dry matter yield and total green matter yield of plots of Guineagrass, an important tropical forage species. The proposed approach, named Deep4Fusion fusion network, can be set to use two different deep learning models. The experimental results indicated that our approach improved the performance between 20% to 33% when compared with standard models reported in previous works, with a significant improvement (p-value < 0.05) for leaf dry matter and total dry matter yield. We believe that the flexibility of multi-view fusion in merging the predictions of several CNNs models through shared layers across the network has the potential to improve the results of many other single-view deep learning approaches.	
Thesaurus Nal: 	Forage dryers; Forage grasses; Forage yield; Phenotype.	
Categoria do assunto: 	--	
Registro original: 	Embrapa Gado de Corte (CNPGC)	
Exemplares: 	CNPGC - (17916-1) - UPC - AP - DD -  -  -  - DIS