Data/hora: 09/06/2024 04:48:04

Provedor de dados: 	111	
País: 	China	
Título: 	AN INTELLIGENT COMPUTER VISION SYSTEM FOR VEGETABLES AND FRUITS QUALITY INSPECTION USING SOFT COMPUTING TECHNIQUES	
Autores: 	G, Narendra V.	
Data: 	2019-10-10	
Ano: 	2019	
Palavras-chave: 	Agriculture and Food quality evaluation; Image processing; Compter Vision; Soft computing Techniques Quality Inspection of fruits and Vegetables; Back propagation neural network; Probabilistic neural network.	
Resumo: 	The quality of food products is very important for the human health. The large population and the increased requirements of food products makes it difficult to arrive the desired quality. The quality inspection and sorting tons of fruits and vegetables manually is a slow, costly, and an inaccurate process. In this research a vision-based quality inspection and sorting system is developed, to increase the quality of food products. The quality inspection and sorting process depends on capturing the image of the fruits/vegetables, analyzing captured image to discard defected products in order to identify the good or bad. Four different systems for different food products have been developed namely, Orange, Lemon, Sweet Lime, and Tomato. A dataset of 1200 images is used to train and test the vision systems (300 images for each). The obtained accuracy ranges from 85.00% to 95.00% for Orange, Lemon, Sweet Lime and Tomato used soft-computing techniques such as Back propagation neural network and Probabilistic neural network.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.cigrjournal.org/index.php/Ejounral/article/view/5188	
Editor: 	International Commission of Agricultural and Biosystems Engineering	
Relação: 	http://www.cigrjournal.org/index.php/Ejounral/article/view/5188/3105

http://www.cigrjournal.org/index.php/Ejounral/article/downloadSuppFile/5188/2317	
Formato: 	application/pdf	
Fonte: 	Agricultural Engineering International: CIGR Journal; Vol 21, No 3 (2019): CIGR Journal; 171-178

1682-1130	
Fonte: 	Copyright (c) 2019 Agricultural Engineering International: CIGR Journal