Smart Monitoring of Crops Using Generative Adversarial Networks

Unmanned aerial vehicles (UAV) are used in precision agriculture (PA) to enable aerial monitoring of farmlands. Intelligent methods are required to pinpoint weed infestations and make optimal choice of pesticide. UAV can fly a multispectral camera and collect data. However, the classification of multispectral images using supervised machine learning algorithms such as convolutional neural networks (CNN) requires a large amount of training data. This is a common drawback in deep learning. Our method makes use of a semi-supervised generative adversarial networks (GAN), providing a pixel-wise classification for all the acquired multispectral images. It consists of a generator network to provide photo-realistic images as extra training data to a multi-class classifier acting as a discriminator and trained on small amounts of labeled data. The performance of the proposed semi-supervised GAN is evaluated on the weedNet dataset consisting of multispectral crop and weed images collected by a micro aerial vehicle (MAV). Results indicate high classification accuracy can be achieved and show the potential of GAN-based methods for the challenging task of multispectral image classification.