Unsupervised deep learning and semi-automatic data labeling in weed discrimination

Abstract

In recent years, supervised Deep Neural Networks have achieved the state-of-the-art in image recognition and this success has spread in many areas. In agricultural field, several researches have been conducted using architectures such as Convolutional Neural Networks. Despite this success, these works are still highly dependent on very time–costly manual data labeling. In contrast to this scenario, Unsupervised Deep Learning has no dependency on data labeling and is targeted as the future of the area, but after a promising start has been obfuscated by the success of supervised networks. Meanwhile, the low-cost of acquisition of field crop imagery using Unnamed Aerial Vehicles could be largely boosted in real-world applications if these images could be annotated without the need for a human specialist. In this work, we tested two recent unsupervised deep clustering algorithms, Joint Unsupervised Learning of Deep Representations and Image Clusters (JULE) and Deep Clustering for Unsupervised Learning of Visual Features (DeepCluster), using two public weed datasets. The first dataset was captured in a soybean plantation in Brazil and discriminates weeds between grass and broadleaf. The second dataset consists of 17,509 labeled images of eight nationally significant weed species native to Australia. We evaluated the purely unsupervised clustering performance using the NMI and Unsupervised Clustering Accuracy metrics and analysed the effects of techniques like data augmentation and transfer learning to improve clustering quality in a broad discussion that can be useful for unsupervised deep clustering in general. We also propose the usage of semi-automatic data labeling which greatly reduces the cost of manual data labeling and can be easily replicated to different datasets. This approach achieved 97% accuracy in discrimination of grass and broadleaf while reducing the number of manual annotations by 100 times, using a custom set of training images, without images labeled using inaccurate clusters.