02844naa a2200385 a 450000100080000000500110000800800410001902400540006010000180011424501330013226000090026550000200027452016660029465000160196065000200197665000200199665000160201665000100203265000080204265300250205065300340207565300180210965300240212765300220215165300200217365300280219365300330222165300280225465300250228265300210230770000200232870000220234870000140237077300740238421199892020-04-17       2020    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1016/j.compag.2020.1052472DOI1 aSANTOS, T. T.  aGrape detection, segmentation, and tracking using deep neural networks and three-dimensional association.h[electronic resource]  c2020  aArticle 105247.  aAgricultural 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.  aAgriculture  aComputer vision  aNeural networks  aAgricultura  aFruta  aUva  aAprendizado profundo  aConvolutional neural networks  aDeep learning  aDetecção de fruta  aDetecção de uva  aFruit detection  aPrevisão de rendimento  aRedes neurais convolucionais  aRedes neurais profundas  aVisão computacional  aYield prediction1 aSOUZA, L. L. de1 aSANTOS, A. A. dos1 aAVILA, S.  tComputers and Electronics in Agriculturegv. 170, p. 1-17, Mar. 2020.