Semi-supervised learning with convolutional neural networks for UAV images automatic recognition

Abstract

The annotation of large datasets is an issue whose challenge increases as the number of labeled samples available to train the classifier reduces in comparison to the amount of unlabeled data. In this context, semi-supervised learning methods aim at discovering and propagating labels to unsupervised samples, such that their correct labeling can improve the classification performance. Our proposal makes use of semi-supervised methodologies to classify an unlabeled training set that is used to train a Convolution Neural Network using different training strategies. The proposed approach is experimentally validated for soybean leaf and herbivorous pest identification using images captured by Unmanned Aerial Vehicles and can support specialists and farmers in the pest control management in soybean fields, especially when they have a limited amount of labeled samples.