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Multi-source data integration for soil mapping using deep learning

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Figure

Figure 1. Representation of the CNN architecture developed in this study for (a) a neuron, (b) the ANN architecture or fully connectedlayer, and (c) the convolutional and pooling layers
Figure 2. Spatial distribution of the observations for each measurement typerecorded per sampling location for the 0–10 cm topsoil
Table 1. Layers used in the sequential model built for topsoil andsubsoil TC prediction.
Figure 4. Effect of the vicinity size of input images. The RMSE (in g 100 g−1) corresponds to the error between the predictions and measuredvalues in the test set.
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