A low-cost system to detect bunches of grapes in natural environment from color images. Reis, M., Morais, R., Pereira, C., Contente, O., Bacelar, M., Soares, S., Valente, A., Baptista, J., Ferreira, P., & Bulas-Cruz, J. Volume 6915 LNCS , 2011. doi abstract bibtex Despite the benefits of precision agriculture and precision viticulture production systems, its adoption rate in the Portuguese Douro Demarcated Region remains low. One of the most demanding tasks in wine making is harvesting. Even for humans, the environment makes grape detection difficult, especially when the grapes and leaves have a similar color, which is generally the case for white grapes. In this paper, we propose a system for the detection and location, in the natural environment, of bunches of grapes in color images. The system is also able to distinguish between white and red grapes, at the same time, it calculates the location of the bunch stem. The proposed system achieved 97% and 91% correct classifications for red and white grapes, respectively. © 2011 Springer-Verlag.
@book{
title = {A low-cost system to detect bunches of grapes in natural environment from color images},
type = {book},
year = {2011},
source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {grape detection,image processing,precision viticulture,visual inspection},
volume = {6915 LNCS},
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abstract = {Despite the benefits of precision agriculture and precision viticulture production systems, its adoption rate in the Portuguese Douro Demarcated Region remains low. One of the most demanding tasks in wine making is harvesting. Even for humans, the environment makes grape detection difficult, especially when the grapes and leaves have a similar color, which is generally the case for white grapes. In this paper, we propose a system for the detection and location, in the natural environment, of bunches of grapes in color images. The system is also able to distinguish between white and red grapes, at the same time, it calculates the location of the bunch stem. The proposed system achieved 97% and 91% correct classifications for red and white grapes, respectively. © 2011 Springer-Verlag.},
bibtype = {book},
author = {Reis, M.J.C.S. and Morais, R. and Pereira, C. and Contente, O. and Bacelar, M. and Soares, S. and Valente, A. and Baptista, J. and Ferreira, P.J.S.G. and Bulas-Cruz, J.},
doi = {10.1007/978-3-642-23687-7_9}
}
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