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Revista dos Trabalhos de Iniciação Científica da UNICAMP
In this work, we modeled the problem of detection of fruit and leaves in viticulture for proximal applications as a supervised machine learning task. We created and manually labeled a database of images obtained at Guaspari Winery. In total, the database consists of 11.883 images of bunch of grapes and leaves. We trained a convolutional network with YOLOv2 architecture to locate and classify bunch of grapes and leaves. Quantitative tests have shown results for detection and classification withdoi:10.20396/revpibic262018155 fatcat:zcwi7knue5et5mmdlxypp4sdvi