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Weed Detection Segmentation Using by Naresh (4 results)

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  • Language: English

    Published by LAP LAMBERT Academic Publishing Okt 2023, 2023

    620679198X / 9786206791980

    • Softcover
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    Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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    Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the recent times, where the automation systems has gained the highest priority to digitalize the world, the field of agriculture plays a major role in the growth of Indian economy. Weed plant detection and segmentation is a new research problem in the field of agriculture. In this paper we present a weed segmentation module. Weed classification module needs image processing task to be performed in order to detect the existence neural network to process the image and various classifiers such as random forest, Decision tree, SVM are used to classify the image the segmentation module makes use of U-Net architecture and Dense CRF is used for post processing in order to make boundaries of object more clear. The performance of the classifiers are measured using standard evaluation metrics. 72 pp. Englisch.…

  • Language: English

    Published by LAP LAMBERT Academic Publishing, 2023

    620679198X / 9786206791980

    • Softcover
    • Print on Demand

    Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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    Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In the recent times, where the automation systems has gained the highest priority to digitalize the world, the field of agriculture plays a major role in the growth of Indian economy. Weed plant detection and segmentation is a new research problem in the field of agriculture. In this paper we present a weed segmentation module. Weed classification module needs image processing task to be performed in order to detect the existence neural network to process the image and various classifiers such as random forest, Decision tree, SVM are used to classify the image the segmentation module makes use of U-Net architecture and Dense CRF is used for post processing in order to make boundaries of object more clear. The performance of the classifiers are measured using standard evaluation metrics.…

  • Language: English

    Published by LAP Lambert Academic Publishing, 2023

    620679198X / 9786206791980

    • Softcover
    • Print on Demand

    Seller: moluna, Greven, Germanymoluna

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    Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In the recent times, where the automation systems has gained the highest priority to digitalize the world, the field of agriculture plays a major role in the growth of Indian economy. Weed plant detection and segmentation is a new research problem in the fi. …

  • Language: English

    Published by LAP LAMBERT Academic Publishing Okt 2023, 2023

    620679198X / 9786206791980

    • Softcover
    • Print on Demand

    Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000

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    Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In the recent times, where the automation systems has gained the highest priority to digitalize the world, the field of agriculture plays a major role in the growth of Indian economy. Weed plant detection and segmentation is a new research problem in the field of agriculture. In this paper we present a weed segmentation module. Weed classification module needs image processing task to be performed in order to detect the existence neural network to process the image and various classifiers such as random forest, Decision tree, SVM are used to classify the image the segmentation module makes use of U-Net architecture and Dense CRF is used for post processing in order to make boundaries of object more clear. The performance of the classifiers are measured using standard evaluation metrics.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Englisch.…