Deep features and One-class classification with unsupervised data for weed detection in UAV images

Abstract

With the raise of the world population, increasing agricultural productivity has become a necessity for farmers. One way to reduce the cost of chemicals and environmental impact is to allocate the right doses of herbicide to the right place and at the right time (precision agriculture). Nowadays, automatic weeds detection is one of the most challenging problem for precision agriculture. However, weeds and crop are hard to discriminate because of their strong similarities. One of the approaches used for weed detection is machine learning. The main common point between machine learning algorithms is the need of training data. In this article we propose to use deep features and one-class classification on unsupervised data for weed detection in UAV images. The results show that one-class classification can be comparable to the literature and also to a deep learning model trained with supervised training data labeling. Results obtained on all test datasets can be up to 90% depending on the data used for the training.