Few-Shot Learning Network For Moving Object Detection Using Exemplar-Based Attention Map

Automating the analysis of soil parameters can optimize the fertilization process, saving time and reducing the costs of food production, leading to a more sustainable agriculture. The work presented in this paper is part of the HYPERVIEW Challenge: Seeing Beyond the Visible. Several methods are proposed, based both on traditional approaches such as Support Vector Regression (SVR) and k-Nearest Neighbors (k-NN), as well as modern neural networks. A parameterized preprocessing stage has been proposed to deal with the varying size of the input data. The best results have been obtained with the k-NN model and the grid division of the images.