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In the segmentation, it was difficult to set the threshold value in their method and the number of training image was also small. One of the disadvantages included that the images in the dataset were captured on a uniform background. Camargo et al. [54] developed a disease segmentation ...
The experiment cannot produce a massive number of diseases as training data. Since disease occurrence in the actual field is usually low, there is a limited number of available datasets to train the deep learning model. However, some generalizations can be made regarding the research workflow ...
The training process is divided into four steps: preprocessing, learning, evaluation, and prediction. The first step is preprocessing. Firstly, the data are preprocessed and handled for better use in subsequent modeling and analysis. Secondly, the data are divided into training data and testing data...
in Remote Sensing for Natural Hazards ManagementAdvanced Machine Learning Approaches for Hyperspectral Data AnalysisAdvanced Radar Signal Processing and ApplicationsAdvanced Remote Sensing Methods for 3D Vegetation Mapping and CharacterizationAdvanced Satellite-Terrestrial NetworksAdvanced Topics in Remote Sensing...