Introduce atrous convolution networks to concatenate, fuse the extractedįeatures, and then export the optimal locations for soil sampling. Key feature extractor, which produces feature maps. In the encoder, the self-attention mechanism is the Our framework isĬonstructed with an encoder-decoder architecture with the self-attention Grounded in the concepts of transformer and self-attention. Network (CNN) backbone, while the second is to innovate a deep-learning design Involves utilizing a state-of-the-art model with the convolutional neural Therefore, we approached the problem with two methods, the first approach The soil sampling dataset isĬhallenging because the ground truth is highly imbalanced binary images. The data for training are collected at different fields in localįarms with five features: aspect, flow accumulation, slope, NDVI (normalizedĭifference vegetation index), and yield.
Processing to find optimal locations that present the important characteristics Download a PDF of the paper titled Deep-Learning Framework for Optimal Selection of Soil Sampling Sites, by Tan-Hanh Pham and 4 other authors Download PDF Abstract: This work leverages the recent advancements of deep learning in image
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