Data/hora: 06/06/2024 17:14:46

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	07/02/2020	
Data da última atualização: 	17/04/2020	
Autoria: 	SANTOS, T. T.; SOUZA, L. L. de; SANTOS, A. A. dos; AVILA, S.	
Afiliação: 	THIAGO TEIXEIRA SANTOS, CNPTIA; LEONARDO L. DE SOUZA, IC/Unicamp; ANDREZA A. DOS SANTOS, IC/Unicamp; SANDRA AVILA, IC/Unicamp.	
Título: 	Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association.	
Ano de publicação: 	2020	
Fonte/Imprenta: 	Computers and Electronics in Agriculture, v. 170, p. 1-17, Mar. 2020.	
DOI: 	https://doi.org/10.1016/j.compag.2020.105247	
Idioma: 	Inglês	
Notas: 	Article 105247.	
Conteúdo: 	Agricultural applications such as yield prediction, precision agriculture and automated harvesting need systems able to infer the crop state from low-cost sensing devices. Proximal sensing using affordable cameras combined with computer vision has seen a promising alternative, strengthened after the advent of convolutional neural networks (CNNs) as an alternative for challenging pattern recognition problems in natural images. Considering fruit growing monitoring and automation, a fundamental problem is the detection, segmentation and counting of individual fruits in orchards. Here we show that for wine grapes, a crop presenting large variability in shape, color, size and compactness, grape clusters can be successfully detected, segmented and tracked using state-of-the-art CNNs. In a test set containing 408 grape clusters from images taken on a trellis-system based vineyard, we have reached an F1-score up to 0.91 for instance segmentation, a fine separation of each cluster from other structures in the image that allows a more accurate assessment of fruit size and shape. We have also shown as clusters can be identified and tracked along video sequences recording orchard rows. We also present a public dataset containing grape clusters properly annotated in 300 images and a novel annotation methodology for segmentation of complex objects in natural images. The presented pipeline for annotation, training, evaluation and tracking of agricultural patterns in images can be replicated for different crops and production systems. It can be employed in the development of sensing components for several agricultural and environmental applications.	
Palavras-Chave: 	Aprendizado profundo; Convolutional neural networks; Deep learning; Detecção de fruta; Detecção de uva; Fruit detection; Previsão de rendimento; Redes neurais convolucionais; Redes neurais profundas; Visão computacional; Yield prediction.	
Thesagro: 	Agricultura; Fruta; Uva.	
Thesaurus Nal: 	Agriculture; Computer vision; Neural networks.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (20442-1) - UPC - AP - DD -  -  -  - DIS