Data/hora: 03/06/2024 15:52:22

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	11/05/2020	
Data da última atualização: 	20/10/2021	
Autoria: 	BOCK, C. H.; BARBEDO, J. G. A.; DEL PONTE, E. M.; BOHNENKAMP, D.; MAHLEIN, A. K.	
Afiliação: 	CLIVE H. BOCK, USDA; JAYME GARCIA ARNAL BARBEDO, CNPTIA; EMERSON M. DEL PONTE, UFV; DAVID BOHNENKAMP, University of Bonn; ANNE-KATRIN MAHLEIN, Institute of Sugar Beet Research, Germany.	
Título: 	From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy.	
Ano de publicação: 	2020	
Fonte/Imprenta: 	Phytopathology Research, v. 2, p. 1-30, 2020.	
DOI: 	https://doi.org/10.1186/s42483-020-00049-8	
Idioma: 	Inglês	
Notas: 	Article 9.	
Conteúdo: 	Abstract. The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.	
Palavras-Chave: 	Acurácia; Aprendizado de máquina; Aprendizado profundo; Assessment; Deep learning; Digital technologies; Dispositivo móvel; Inteligência artificial; Machine learning; Mobile device; Phenotyping; Precisão; Sensor; Severidade da doença; Tecnologias digitais.	
Thesagro: 	Doença de Planta.	
Thesaurus Nal: 	Accuracy; Artificial intelligence; Disease severity; Plant diseases and disorders; Precision; Precision agriculture.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/212860/1/AP-Phytopathology-Research-2020.pdf	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (20461-1) - UPC - AP - DD -  -  -  - DIS