Comparison of deep learning methods for grapevine growth stage recognition. Schieck, M., Krajsic, P., Loos, F., Hussein, A., Franczyk, B., Kozierkiewicz, A., & Pietranik, M. Computers and Electronics in Agriculture, 211:107944, Elsevier, 8, 2023.
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Paper doi abstract bibtex
Paper doi abstract bibtex Monitoring the phenological development stages of grapes represents a challenge in viticulture. It includes the phenological distinction of the growth stages of grapevines and the continuous technological developments, especially in computer vision, enabling a detailed classification of economically relevant development stages of grapes. In the present work, we show that based on a cascading computer vision approach, the development stages of grapes can be classified and distinguished at the micro level. In a comparative experiment (ResNet, DenseNet, InceptionV3), it could be shown that a ResNet architecture provides the best classification results with an average accuracy of 88.1%.
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abstract = {Monitoring the phenological development stages of grapes represents a challenge in viticulture. It includes the phenological distinction of the growth stages of grapevines and the continuous technological developments, especially in computer vision, enabling a detailed classification of economically relevant development stages of grapes. In the present work, we show that based on a cascading computer vision approach, the development stages of grapes can be classified and distinguished at the micro level. In a comparative experiment (ResNet, DenseNet, InceptionV3), it could be shown that a ResNet architecture provides the best classification results with an average accuracy of 88.1%.},
bibtype = {article},
author = {Schieck, Martin and Krajsic, Philippe and Loos, Felix and Hussein, Abdulbaree and Franczyk, Bogdan and Kozierkiewicz, Adrianna and Pietranik, Marcin},
doi = {10.1016/J.COMPAG.2023.107944},
journal = {Computers and Electronics in Agriculture}
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