Data/hora: 14/06/2024 16:02:46

Biblioteca(s): 	Embrapa Uva e Vinho.	
Data corrente: 	30/08/2016	
Data da última atualização: 	08/03/2019	
Autoria: 	NACHTIGALL, L. G.; ARAUJO, R. M.; NACHTIGALL, G. R.	
Afiliação: 	GILMAR RIBEIRO NACHTIGALL, CNPUV.	
Título: 	Classification of apple tree disorders using Convolutional Neural Networks.	
Ano de publicação: 	2016	
Fonte/Imprenta: 	In: INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENSE, 28., 2016, San Jose, United States. Anais...San Jose, United States: IEEE, Paper Submission 127, p. 472-476, 2016.	
Idioma: 	Português	
Conteúdo: 	Abstract?This paper studies the use of Convolutional Neural Networks to automatically detect and classify diseases, nutritional deficiencies and damage by herbicides on apple trees from images of their leaves. This task is fundamental to guarantee a high quality of the resulting yields and is currently largely performed by experts in the field, which can severely limit scale and add to costs. By using a novel data set containing labeled examples consisting of 2539 images from 6 known disorders, we show that trained Convolutional Neural Networks are able to match or outperform experts in this task, achieving a 97.3% accuracy on a hold-out set.	
Palavras-Chave: 	Apple trees; Convolutional Neural Networks; Damage; Diseases; Herbicide; Macieira; Nutritional deficiencies; Redes neurais.	
Thesagro: 	Maca.	
Categoria do assunto: 	--	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/item/157421/1/Nachtigall-IEE28-2016-472-476.pdf	
Registro original: 	Embrapa Uva e Vinho (CNPUV)	
Exemplares: 	CNPUV - (16715-1) - UPC - AA - DD -  -  -  - DIS