Inside Out: Transforming Images of Lab-Grown Plants for Machine Learning Applications in Agriculture

Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical differences between two plants of the same genotype, often as a result of differing growing conditions. Synthetically-augmented datasets have shown promise in improving existing models when real data is not available. In this paper, we employ a contrastive unpaired translation (CUT) generative adversarial network (GAN) and simple image processing techniques to translate indoor plant images to appear as field images. While we train our network to translate an image containing only a single plant, we show that our method is easily extendable to produce multiple-plant field images. Furthermore, we use our synthetic multi-plant images to train several YoloV5 nano object detection models to perform the task of plant detection and measure the accuracy of the model on real field data images. Including training data generated by the CUT-GAN leads to better plant detection performance compared to a network trained solely on real data.Comment: 35 pages, 23 figure

Congenial bedfellows? The academy and the antiquities trade

The illicit trade in antiquities and other cultural objects is socially harmful in several respects. Private collectors and museums are generally considered culpable in providing end demand by acquiring illicitly traded objects, but this article suggests that the facilitating actions of academic experts have previously been overlooked. Through a series of case studies, it examines different ways in which academic expertise is indispensable for the efficient functioning of the trade and suggests that a knowledge-based ethical environment for academic practice would allow scholars to make more informed choices about the propriety or otherwise of their involvement with the trade