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• W3175509189 abstract "Estimating the plant population after the emergence of plants seems to be the most accurate way to make a fair judgment about the quality of the sowing across a field. A pixel-level classification model based on deep learning methods was used to predict each pixel in the image to recognise the location and population of plants. A U-Net based model was developed to determine two-class pixels of the emergence point of plants and background. A total of 3890 images were captured from two cereal fields. To prepare data for the training, a collection of 243 images from field 1 and 196 images from field 2 were selected at random and marked with associated growth points. During the training process, some transformations were randomly carried out on the input images and the model was trained on the augmented images. A share of 0.33% of the data set (255 images) was split up as a test data set. The training process continued with 50 runs of the entire input data, that is 48 epochs. The U-Net model was therefore successfully developed and trained for emergence point detection. In a further step, the sensitivity of the developed model to the threshold values and the size of emergence points were investigated. Two approaches to a plant location system and the plant population counting system were considered. When comparing different combination results, the most suitable growth point diameters and threshold values for the two approaches were successfully determined." @default.
• W3175509189 created "2021-07-05" @default.
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• W3175509189 date "2021-08-01" @default.
• W3175509189 modified "2023-09-23" @default.
• W3175509189 title "Development of pixel-wise U-Net model to assess performance of cereal sowing" @default.
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• W3175509189 doi "https://doi.org/10.1016/j.biosystemseng.2021.06.006" @default.
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