Automatic Detection of White Grapes in Natural Environment Using Image Processing. Reis, M., C., M., Morais, R., Pereira, C., Soares, S., Valente, A., Baptista, J., Ferreira, P., & Cruz, J. Advances in Intelligent and Soft Computing, 87:19-26, 2011. ![Automatic Detection of White Grapes in Natural Environment Using Image Processing [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex The rate of adoption of Precision Agriculture and Precision Viticulture production systems in the Douro Demarcated Region remains low. We believe that one way to raise it is to address challenging real-world problems whose solution offers a clear benefit to the viticulturist. For example, one of the most demanding tasks in wine making is harvesting. Even for humans, the detection of grapes in their natural environment is not always easy. White grapes are particularly difficult to detect, since their color is similar to that of the leafs. Here we present a low cost system for the detection of white grapes in natural environment color images. The system also calculates the probable location of the bunch stem and achieves 91% of correct classifications. © 2011 Springer-Verlag Berlin Heidelberg.
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title = {Automatic Detection of White Grapes in Natural Environment Using Image Processing},
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year = {2011},
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abstract = {The rate of adoption of Precision Agriculture and Precision Viticulture production systems in the Douro Demarcated Region remains low. We believe that one way to raise it is to address challenging real-world problems whose solution offers a clear benefit to the viticulturist. For example, one of the most demanding tasks in wine making is harvesting. Even for humans, the detection of grapes in their natural environment is not always easy. White grapes are particularly difficult to detect, since their color is similar to that of the leafs. Here we present a low cost system for the detection of white grapes in natural environment color images. The system also calculates the probable location of the bunch stem and achieves 91% of correct classifications. © 2011 Springer-Verlag Berlin Heidelberg.},
bibtype = {article},
author = {Reis, Manuel Cabral M.C. and Morais, Raul and Pereira, Carlos and Soares, S. and Valente, A. and Baptista, J. and Ferreira, P.J.S.G. and Cruz, J.B.},
doi = {10.1007/978-3-642-19644-7_3},
journal = {Advances in Intelligent and Soft Computing}
}
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